Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

NASA Astrophysics Data System (ADS)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Evaluation of Workpiece Temperature during Drilling of GLARE Fiber Metal Laminates Using Infrared Techniques: Effect of Cutting Parameters, Fiber Orientation and Spray Mist Application.

PubMed

Giasin, Khaled; Ayvar-Soberanis, Sabino

2016-07-28

The rise in cutting temperatures during the machining process can influence the final quality of the machined part. The impact of cutting temperatures is more critical when machining composite-metal stacks and fiber metal laminates due to the stacking nature of those hybrids which subjects the composite to heat from direct contact with metallic part of the stack and the evacuated hot chips. In this paper, the workpiece surface temperature of two grades of fiber metal laminates commercially know as GLARE is investigated. An experimental study was carried out using thermocouples and infrared thermography to determine the emissivity of the upper, lower and side surfaces of GLARE laminates. In addition, infrared thermography was used to determine the maximum temperature of the bottom surface of machined holes during drilling GLARE under dry and minimum quantity lubrication (MQL) cooling conditions under different cutting parameters. The results showed that during the machining process, the workpiece surface temperature increased with the increase in feed rate and fiber orientation influenced the developed temperature in the laminate.

Evaluation of Workpiece Temperature during Drilling of GLARE Fiber Metal Laminates Using Infrared Techniques: Effect of Cutting Parameters, Fiber Orientation and Spray Mist Application

PubMed Central

Giasin, Khaled; Ayvar-Soberanis, Sabino

2016-01-01

The rise in cutting temperatures during the machining process can influence the final quality of the machined part. The impact of cutting temperatures is more critical when machining composite-metal stacks and fiber metal laminates due to the stacking nature of those hybrids which subjects the composite to heat from direct contact with metallic part of the stack and the evacuated hot chips. In this paper, the workpiece surface temperature of two grades of fiber metal laminates commercially know as GLARE is investigated. An experimental study was carried out using thermocouples and infrared thermography to determine the emissivity of the upper, lower and side surfaces of GLARE laminates. In addition, infrared thermography was used to determine the maximum temperature of the bottom surface of machined holes during drilling GLARE under dry and minimum quantity lubrication (MQL) cooling conditions under different cutting parameters. The results showed that during the machining process, the workpiece surface temperature increased with the increase in feed rate and fiber orientation influenced the developed temperature in the laminate. PMID:28773757

Does machine perfusion decrease ischemia reperfusion injury?

PubMed

Bon, D; Delpech, P-O; Chatauret, N; Hauet, T; Badet, L; Barrou, B

2014-06-01

In 1990's, use of machine perfusion for organ preservation has been abandoned because of improvement of preservation solutions, efficient without perfusion, easy to use and cheaper. Since the last 15 years, a renewed interest for machine perfusion emerged based on studies performed on preclinical model and seems to make consensus in case of expanded criteria donors or deceased after cardiac death donations. We present relevant studies highlighted the efficiency of preservation with hypothermic machine perfusion compared to static cold storage. Machines for organ preservation being in constant evolution, we also summarized recent developments included direct oxygenation of the perfusat. Machine perfusion technology also enables organ reconditioning during the last hours of preservation through a short period of perfusion on hypothermia, subnormothermia or normothermia. We present significant or low advantages for machine perfusion against ischemia reperfusion injuries regarding at least one primary parameter: risk of DFG, organ function or graft survival. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Determination of Optimum Cutting Parameters for Surface Roughness in Turning AL-B4C Composites

NASA Astrophysics Data System (ADS)

Channabasavaraja, H. K.; Nagaraj, P. M.; Srinivasan, D.

2016-09-01

Many materials such as alloys, composites find their applications on the basis of machinability, cost and availability. In the present work, machinability of Aluminium 1100 and Boron carbide (AL+ B4C) composite material is examined by using lathe tool dynometers (BANKA Lathe) by varying the cutting parameters like spindle speed, Depth of cut and Feed rate in 3 levels. Also, surface roughness is measured against the weight % of reinforcement in the composite (0, 4 and 8 %). From the study it is observed that the hardness of a composite material increases with increase in weight % of reinforcement material (B4C) by 26.27 and 66.7 % respectively. The addition of reinforcement materials influences the machinability. The cutting force in both X and Z direction were also found increment with the reinforcement percentage.

Running accuracy analysis of a 3-RRR parallel kinematic machine considering the deformations of the links

NASA Astrophysics Data System (ADS)

Wang, Liping; Jiang, Yao; Li, Tiemin

2014-09-01

Parallel kinematic machines have drawn considerable attention and have been widely used in some special fields. However, high precision is still one of the challenges when they are used for advanced machine tools. One of the main reasons is that the kinematic chains of parallel kinematic machines are composed of elongated links that can easily suffer deformations, especially at high speeds and under heavy loads. A 3-RRR parallel kinematic machine is taken as a study object for investigating its accuracy with the consideration of the deformations of its links during the motion process. Based on the dynamic model constructed by the Newton-Euler method, all the inertia loads and constraint forces of the links are computed and their deformations are derived. Then the kinematic errors of the machine are derived with the consideration of the deformations of the links. Through further derivation, the accuracy of the machine is given in a simple explicit expression, which will be helpful to increase the calculating speed. The accuracy of this machine when following a selected circle path is simulated. The influences of magnitude of the maximum acceleration and external loads on the running accuracy of the machine are investigated. The results show that the external loads will deteriorate the accuracy of the machine tremendously when their direction coincides with the direction of the worst stiffness of the machine. The proposed method provides a solution for predicting the running accuracy of the parallel kinematic machines and can also be used in their design optimization as well as selection of suitable running parameters.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Belley, M; Schmidt, M; Knutson, N

Purpose: Physics second-checks for external beam radiation therapy are performed, in-part, to verify that the machine parameters in the Record-and-Verify (R&V) system that will ultimately be sent to the LINAC exactly match the values initially calculated by the Treatment Planning System (TPS). While performing the second-check, a large portion of the physicists’ time is spent navigating and arranging display windows to locate and compare the relevant numerical values (MLC position, collimator rotation, field size, MU, etc.). Here, we describe the development of a software tool that guides the physicist by aggregating and succinctly displaying machine parameter data relevant to themore » physics second-check process. Methods: A data retrieval software tool was developed using Python to aggregate data and generate a list of machine parameters that are commonly verified during the physics second-check process. This software tool imported values from (i) the TPS RT Plan DICOM file and (ii) the MOSAIQ (R&V) Structured Query Language (SQL) database. The machine parameters aggregated for this study included: MLC positions, X&Y jaw positions, collimator rotation, gantry rotation, MU, dose rate, wedges and accessories, cumulative dose, energy, machine name, couch angle, and more. Results: A GUI interface was developed to generate a side-by-side display of the aggregated machine parameter values for each field, and presented to the physicist for direct visual comparison. This software tool was tested for 3D conformal, static IMRT, sliding window IMRT, and VMAT treatment plans. Conclusion: This software tool facilitated the data collection process needed in order for the physicist to conduct a second-check, thus yielding an optimized second-check workflow that was both more user friendly and time-efficient. Utilizing this software tool, the physicist was able to spend less time searching through the TPS PDF plan document and the R&V system and focus the second-check efforts on assessing the patient-specific plan-quality.« less

Analysis of Shield Construction in Spherical Weathered Granite Development Area

NASA Astrophysics Data System (ADS)

Cao, Quan; Li, Peigang; Gong, Shuhua

2018-01-01

The distribution of spherical weathered bodies (commonly known as "boulder") in the granite development area directly affects the shield construction of urban rail transit engineering. This paper is based on the case of shield construction of granite globular development area in Southern China area, the parameter control in shield machine selection and shield advancing during the shield tunneling in this special geological environment is analyzed. And it is suggested that shield machine should be selected for shield construction of granite spherical weathered zone. Driving speed, cutter torque, shield machine thrust, the amount of penetration and the speed of the cutter head of shield machine should be controlled when driving the boulder formation, in order to achieve smooth excavation and reduce the disturbance to the formation.

A Modelling Method of Bolt Joints Based on Basic Characteristic Parameters of Joint Surfaces

NASA Astrophysics Data System (ADS)

Yuansheng, Li; Guangpeng, Zhang; Zhen, Zhang; Ping, Wang

2018-02-01

Bolt joints are common in machine tools and have a direct impact on the overall performance of the tools. Therefore, the understanding of bolt joint characteristics is essential for improving machine design and assembly. Firstly, According to the experimental data obtained from the experiment, the stiffness curve formula was fitted. Secondly, a finite element model of unit bolt joints such as bolt flange joints, bolt head joints, and thread joints was constructed, and lastly the stiffness parameters of joint surfaces were implemented in the model by the secondary development of ABAQUS. The finite element model of the bolt joint established by this method can simulate the contact state very well.

Directly spheroidizing during hot deformation in GCr15 steels

NASA Astrophysics Data System (ADS)

Zhu, Guo-hui; Zheng, Gang

2008-03-01

The spheroidizing heat treatment is normally required prior to the cold forming in GCr15 steel in order to improve its machinability. In the conventional spheroidizing process, very long annealing time, generally more than 10 h, is needed to assure proper spheroidizing. It results in low productivity, high cost, and especially high energy consumption. Therefore, the possibility of directly spheroidizing during hot deformation in GCr15 steel is preliminarily explored. The effect of hot deformation parameters on the final microstructure and hardness is investigated systematically in order to develop a directly spheroidizing technology. Experimental results illustrate that low deformation temperature and slow cooling rate is the favorite in directly softening and/or spheroidizing during hot deformation, which allows the properties of asrolled GCr15 to be applicable for post-machining without requirement of prior annealing.

Numerical investigation and electro-acoustic modeling of measurement methods for the in-duct acoustical source parameters.

PubMed

Jang, Seung-Ho; Ih, Jeong-Guon

2003-02-01

It is known that the direct method yields different results from the indirect (or load) method in measuring the in-duct acoustic source parameters of fluid machines. The load method usually comes up with a negative source resistance, although a fairly accurate prediction of radiated noise can be obtained from any method. This study is focused on the effect of the time-varying nature of fluid machines on the output results of two typical measurement methods. For this purpose, a simplified fluid machine consisting of a reservoir, a valve, and an exhaust pipe is considered as representing a typical periodic, time-varying system and the measurement situations are simulated by using the method of characteristics. The equivalent circuits for such simulations are also analyzed by considering the system as having a linear time-varying source. It is found that the results from the load method are quite sensitive to the change of cylinder pressure or valve profile, in contrast to those from the direct method. In the load method, the source admittance turns out to be predominantly dependent on the valve admittance at the calculation frequency as well as the valve and load admittances at other frequencies. In the direct method, however, the source resistance is always positive and the source admittance depends mainly upon the zeroth order of valve admittance.

Thermocouple and infrared sensor-based measurement of temperature distribution in metal cutting.

PubMed

Kus, Abdil; Isik, Yahya; Cakir, M Cemal; Coşkun, Salih; Özdemir, Kadir

2015-01-12

In metal cutting, the magnitude of the temperature at the tool-chip interface is a function of the cutting parameters. This temperature directly affects production; therefore, increased research on the role of cutting temperatures can lead to improved machining operations. In this study, tool temperature was estimated by simultaneous temperature measurement employing both a K-type thermocouple and an infrared radiation (IR) pyrometer to measure the tool-chip interface temperature. Due to the complexity of the machining processes, the integration of different measuring techniques was necessary in order to obtain consistent temperature data. The thermal analysis results were compared via the ANSYS finite element method. Experiments were carried out in dry machining using workpiece material of AISI 4140 alloy steel that was heat treated by an induction process to a hardness of 50 HRC. A PVD TiAlN-TiN-coated WNVG 080404-IC907 carbide insert was used during the turning process. The results showed that with increasing cutting speed, feed rate and depth of cut, the tool temperature increased; the cutting speed was found to be the most effective parameter in assessing the temperature rise. The heat distribution of the cutting tool, tool-chip interface and workpiece provided effective and useful data for the optimization of selected cutting parameters during orthogonal machining.

Thermocouple and Infrared Sensor-Based Measurement of Temperature Distribution in Metal Cutting

PubMed Central

Kus, Abdil; Isik, Yahya; Cakir, M. Cemal; Coşkun, Salih; Özdemir, Kadir

2015-01-01

In metal cutting, the magnitude of the temperature at the tool-chip interface is a function of the cutting parameters. This temperature directly affects production; therefore, increased research on the role of cutting temperatures can lead to improved machining operations. In this study, tool temperature was estimated by simultaneous temperature measurement employing both a K-type thermocouple and an infrared radiation (IR) pyrometer to measure the tool-chip interface temperature. Due to the complexity of the machining processes, the integration of different measuring techniques was necessary in order to obtain consistent temperature data. The thermal analysis results were compared via the ANSYS finite element method. Experiments were carried out in dry machining using workpiece material of AISI 4140 alloy steel that was heat treated by an induction process to a hardness of 50 HRC. A PVD TiAlN-TiN-coated WNVG 080404-IC907 carbide insert was used during the turning process. The results showed that with increasing cutting speed, feed rate and depth of cut, the tool temperature increased; the cutting speed was found to be the most effective parameter in assessing the temperature rise. The heat distribution of the cutting tool, tool-chip interface and workpiece provided effective and useful data for the optimization of selected cutting parameters during orthogonal machining. PMID:25587976

Theoretical Analysis of Spacing Parameters of Anisotropic 3D Surface Roughness

NASA Astrophysics Data System (ADS)

Rudzitis, J.; Bulaha, N.; Lungevics, J.; Linins, O.; Berzins, K.

2017-04-01

The authors of the research have analysed spacing parameters of anisotropic 3D surface roughness crosswise to machining (friction) traces RSm1 and lengthwise to machining (friction) traces RSm2. The main issue arises from the RSm2 values being limited by values of sampling length l in the measuring devices; however, on many occasions RSm2 values can exceed l values. Therefore, the mean spacing values of profile irregularities in the longitudinal direction in many cases are not reliable and they should be determined by another method. Theoretically, it is proved that anisotropic surface roughness anisotropy coefficient c=RSm1/RSm2 equals texture aspect ratio Str, which is determined by surface texture standard EN ISO 25178-2. This allows using parameter Str to determine mean spacing of profile irregularities and estimate roughness anisotropy.

Reliability Analysis of Uniaxially Ground Brittle Materials

NASA Technical Reports Server (NTRS)

Salem, Jonathan A.; Nemeth, Noel N.; Powers, Lynn M.; Choi, Sung R.

1995-01-01

The fast fracture strength distribution of uniaxially ground, alpha silicon carbide was investigated as a function of grinding angle relative to the principal stress direction in flexure. Both as-ground and ground/annealed surfaces were investigated. The resulting flexural strength distributions were used to verify reliability models and predict the strength distribution of larger plate specimens tested in biaxial flexure. Complete fractography was done on the specimens. Failures occurred from agglomerates, machining cracks, or hybrid flaws that consisted of a machining crack located at a processing agglomerate. Annealing eliminated failures due to machining damage. Reliability analyses were performed using two and three parameter Weibull and Batdorf methodologies. The Weibull size effect was demonstrated for machining flaws. Mixed mode reliability models reasonably predicted the strength distributions of uniaxial flexure and biaxial plate specimens.

Precision diamond grinding of ceramics and glass

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, S.; Paul, H.; Scattergood, R.O.

A new research initiative will be undertaken to investigate the effect of machine parameters and material properties on precision diamond grinding of ceramics and glass. The critical grinding depth to initiate the plastic flow-to-brittle fracture regime will be directly measured using plunge-grind tests. This information will be correlated with machine parameters such as wheel bonding and diamond grain size. Multiaxis grinding tests will then be made to provide data more closely coupled with production technology. One important aspect of the material property studies involves measuring fracture toughness at the very short crack sizes commensurate with grinding damage. Short crack toughnessmore » value`s can be much less than the long-crack toughness values measured in conventional fracture tests.« less

Crystal Orientation Effect on the Subsurface Deformation of Monocrystalline Germanium in Nanometric Cutting.

PubMed

Lai, Min; Zhang, Xiaodong; Fang, Fengzhou

2017-12-01

Molecular dynamics simulations of nanometric cutting on monocrystalline germanium are conducted to investigate the subsurface deformation during and after nanometric cutting. The continuous random network model of amorphous germanium is established by molecular dynamics simulation, and its characteristic parameters are extracted to compare with those of the machined deformed layer. The coordination number distribution and radial distribution function (RDF) show that the machined surface presents the similar amorphous state. The anisotropic subsurface deformation is studied by nanometric cutting on the (010), (101), and (111) crystal planes of germanium, respectively. The deformed structures are prone to extend along the 110 slip system, which leads to the difference in the shape and thickness of the deformed layer on various directions and crystal planes. On machined surface, the greater thickness of subsurface deformed layer induces the greater surface recovery height. In order to get the critical thickness limit of deformed layer on machined surface of germanium, the optimized cutting direction on each crystal plane is suggested according to the relevance of the nanometric cutting to the nanoindentation.

FSW of Aluminum Tailor Welded Blanks across Machine Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hovanski, Yuri; Upadhyay, Piyush; Carlson, Blair

2015-02-16

Development and characterization of friction stir welded aluminum tailor welded blanks was successfully carried out on three separate machine platforms. Each was a commercially available, gantry style, multi-axis machine designed specifically for friction stir welding. Weld parameters were developed to support high volume production of dissimilar thickness aluminum tailor welded blanks at speeds of 3 m/min and greater. Parameters originally developed on an ultra-high stiffness servo driven machine where first transferred to a high stiffness servo-hydraulic friction stir welding machine, and subsequently transferred to a purpose built machine designed to accommodate thin sheet aluminum welding. The inherent beam stiffness, bearingmore » compliance, and control system for each machine were distinctly unique, which posed specific challenges in transferring welding parameters across machine platforms. This work documents the challenges imposed by successfully transferring weld parameters from machine to machine, produced from different manufacturers and with unique control systems and interfaces.« less

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Development of an Empirical Model for Optimization of Machining Parameters to Minimize Power Consumption

NASA Astrophysics Data System (ADS)

Kant Garg, Girish; Garg, Suman; Sangwan, K. S.

2018-04-01

The manufacturing sector consumes huge energy demand and the machine tools used in this sector have very less energy efficiency. Selection of the optimum machining parameters for machine tools is significant for energy saving and for reduction of environmental emission. In this work an empirical model is developed to minimize the power consumption using response surface methodology. The experiments are performed on a lathe machine tool during the turning of AISI 6061 Aluminum with coated tungsten inserts. The relationship between the power consumption and machining parameters is adequately modeled. This model is used for formulation of minimum power consumption criterion as a function of optimal machining parameters using desirability function approach. The influence of machining parameters on the energy consumption has been found using the analysis of variance. The validation of the developed empirical model is proved using the confirmation experiments. The results indicate that the developed model is effective and has potential to be adopted by the industry for minimum power consumption of machine tools.

Productivity improvement through cycle time analysis

NASA Astrophysics Data System (ADS)

Bonal, Javier; Rios, Luis; Ortega, Carlos; Aparicio, Santiago; Fernandez, Manuel; Rosendo, Maria; Sanchez, Alejandro; Malvar, Sergio

1996-09-01

A cycle time (CT) reduction methodology has been developed in the Lucent Technology facility (former AT&T) in Madrid, Spain. It is based on a comparison of the contribution of each process step in each technology with a target generated by a cycle time model. These targeted cycle times are obtained using capacity data of the machines processing those steps, queuing theory and theory of constrains (TOC) principles (buffers to protect bottleneck and low cycle time/inventory everywhere else). Overall efficiency equipment (OEE) like analysis is done in the machine groups with major differences between their target cycle time and real values. Comparisons between the current value of the parameters that command their capacity (process times, availability, idles, reworks, etc.) and the engineering standards are done to detect the cause of exceeding their contribution to the cycle time. Several friendly and graphical tools have been developed to track and analyze those capacity parameters. Specially important have showed to be two tools: ASAP (analysis of scheduling, arrivals and performance) and performer which analyzes interrelation problems among machines procedures and direct labor. The performer is designed for a detailed and daily analysis of an isolate machine. The extensive use of this tool by the whole labor force has demonstrated impressive results in the elimination of multiple small inefficiencies with a direct positive implications on OEE. As for ASAP, it shows the lot in process/queue for different machines at the same time. ASAP is a powerful tool to analyze the product flow management and the assigned capacity for interdependent operations like the cleaning and the oxidation/diffusion. Additional tools have been developed to track, analyze and improve the process times and the availability.

Diamond Tool Specific Wear Rate Assessment in Granite Machining by Means of Knoop Micro-Hardness and Process Parameters

NASA Astrophysics Data System (ADS)

Goktan, R. M.; Gunes Yılmaz, N.

2017-09-01

The present study was undertaken to investigate the potential usability of Knoop micro-hardness, both as a single parameter and in combination with operational parameters, for sawblade specific wear rate (SWR) assessment in the machining of ornamental granites. The sawing tests were performed on different commercially available granite varieties by using a fully instrumented side-cutting machine. During the sawing tests, two fundamental productivity parameters, namely the workpiece feed rate and cutting depth, were varied at different levels. The good correspondence observed between the measured Knoop hardness and SWR values for different operational conditions indicates that it has the potential to be used as a rock material property that can be employed in preliminary wear estimations of diamond sawblades. Also, a multiple regression model directed to SWR prediction was developed which takes into account the Knoop hardness, cutting depth and workpiece feed rate. The relative contribution of each independent variable in the prediction of SWR was determined by using test statistics. The prediction accuracy of the established model was checked against new observations. The strong prediction performance of the model suggests that its framework may be applied to other granites and operational conditions for quantifying or differentiating the relative wear performance of diamond sawblades.

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

NASA Astrophysics Data System (ADS)

Zhou, Ming; Wu, Jianyang; Xu, Xiaoyi; Mu, Xin; Dou, Yunping

2018-02-01

In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

Fuzzy logic controller optimization

DOEpatents

Sepe, Jr., Raymond B; Miller, John Michael

2004-03-23

A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

Machine learning phases of matter

NASA Astrophysics Data System (ADS)

Carrasquilla, Juan; Melko, Roger G.

2017-02-01

Condensed-matter physics is the study of the collective behaviour of infinitely complex assemblies of electrons, nuclei, magnetic moments, atoms or qubits. This complexity is reflected in the size of the state space, which grows exponentially with the number of particles, reminiscent of the `curse of dimensionality' commonly encountered in machine learning. Despite this curse, the machine learning community has developed techniques with remarkable abilities to recognize, classify, and characterize complex sets of data. Here, we show that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians. Readily programmable through modern software libraries, neural networks can be trained to detect multiple types of order parameter, as well as highly non-trivial states with no conventional order, directly from raw state configurations sampled with Monte Carlo.

Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM

NASA Astrophysics Data System (ADS)

Sheng, Hanlin; Zhang, Tianhong

2017-08-01

In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm - gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.

The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

PubMed

Kruse, Christian

2018-06-01

To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature. Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue. Unsupervised machine learning can allow us to understand patterns in data between diabetic pathophysiology and altered bone metabolism. Image analysis using deep learning can allow us to be less dependent on surrogate predictors and use large volumes of images to classify diabetes-induced osteoporosis and predict future outcomes directly from images. "Big Data" techniques herald new possibilities to understand diabetes-induced osteoporosis and ascertain our current ability to classify, understand, and predict this condition.

Modeling of solid-state and excimer laser processes for 3D micromachining

NASA Astrophysics Data System (ADS)

Holmes, Andrew S.; Onischenko, Alexander I.; George, David S.; Pedder, James E.

2005-04-01

An efficient simulation method has recently been developed for multi-pulse ablation processes. This is based on pulse-by-pulse propagation of the machined surface according to one of several phenomenological models for the laser-material interaction. The technique allows quantitative predictions to be made about the surface shapes of complex machined parts, given only a minimal set of input data for parameter calibration. In the case of direct-write machining of polymers or glasses with ns-duration pulses, this data set can typically be limited to the surface profiles of a small number of standard test patterns. The use of phenomenological models for the laser-material interaction, calibrated by experimental feedback, allows fast simulation, and can achieve a high degree of accuracy for certain combinations of material, laser and geometry. In this paper, the capabilities and limitations of the approach are discussed, and recent results are presented for structures machined in SU8 photoresist.

On the Use of Machine Learning Techniques for the Mechanical Characterization of Soft Biological Tissues.

PubMed

Cilla, M; Pérez-Rey, I; Martínez, M A; Peña, Estefania; Martínez, Javier

2018-06-23

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the SEF and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted, thus the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues. This article is protected by copyright. All rights reserved.

Method and system for controlling a permanent magnet machine

DOEpatents

Walters, James E.

2003-05-20

Method and system for controlling the start of a permanent magnet machine are provided. The method allows to assign a parameter value indicative of an estimated initial rotor position of the machine. The method further allows to energize the machine with a level of current being sufficiently high to start rotor motion in a desired direction in the event the initial rotor position estimate is sufficiently close to the actual rotor position of the machine. A sensing action allows to sense whether any incremental changes in rotor position occur in response to the energizing action. In the event no changes in rotor position are sensed, the method allows to incrementally adjust the estimated rotor position by a first set of angular values until changes in rotor position are sensed. In the event changes in rotor position are sensed, the method allows to provide a rotor alignment signal as rotor motion continues. The alignment signal allows to align the estimated rotor position relative to the actual rotor position. This alignment action allows for operating the machine over a wide speed range.

Experimental analysis on semi-finishing machining of Ti6Al4V additively manufactured by direct melting laser sintering

NASA Astrophysics Data System (ADS)

Imbrogno, Stano; Bordin, Alberto; Bruschi, Stefania; Umbrello, Domenico

2016-10-01

The Additive Manufacturing (AM) techniques are particularly appealing especially for titanium aerospace and biomedical components because they permit to achieve a strong reduction of the buy-to-fly ratio. However, finishing machining operations are often necessary to reduce the uneven surface roughness and geometrics because of local missing accuracy. This work shows the influence of the cutting parameters, cutting speed and feed rate, on the cutting forces as well as on the thermal field observed in the cutting zone, during a turning operation carried out on bars made of Ti6Al4V obtained by the AM process called Direct Metal Laser Sintering (DMLS). Moreover, the sub-surface microstructure alterations due to the process are also showed and commented.

Performance Monitoring Of A Computer Numerically Controlled (CNC) Lathe Using Pattern Recognition Techniques

NASA Astrophysics Data System (ADS)

Daneshmend, L. K.; Pak, H. A.

1984-02-01

On-line monitoring of the cutting process in CNC lathe is desirable to ensure unattended fault-free operation in an automated environment. The state of the cutting tool is one of the most important parameters which characterises the cutting process. Direct monitoring of the cutting tool or workpiece is not feasible during machining. However several variables related to the state of the tool can be measured on-line. A novel monitoring technique is presented which uses cutting torque as the variable for on-line monitoring. A classifier is designed on the basis of the empirical relationship between cutting torque and flank wear. The empirical model required by the on-line classifier is established during an automated training cycle using machine vision for off-line direct inspection of the tool.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

PubMed

Ruiz Hidalgo, Irene; Rodriguez, Pablo; Rozema, Jos J; Ní Dhubhghaill, Sorcha; Zakaria, Nadia; Tassignon, Marie-José; Koppen, Carina

2016-06-01

To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with other known keratoconus (KC) classification methods. Pentacam data from 860 eyes were included in the study and divided into 5 groups: 454 KC, 67 forme fruste (FF), 28 astigmatic, 117 after refractive surgery (PR), and 194 normal eyes (N). Twenty-two parameters were used for classification using a support vector machine algorithm developed in Weka, a machine-learning computer software. The cross-validation accuracy for 3 different classification tasks (KC vs. N, FF vs. N and all 5 groups) was calculated and compared with other known classification methods. The accuracy achieved in the KC versus N discrimination task was 98.9%, with 99.1% sensitivity and 98.5% specificity for KC detection. The accuracy in the FF versus N task was 93.1%, with 79.1% sensitivity and 97.9% specificity for the FF discrimination. Finally, for the 5-groups classification, the accuracy was 88.8%, with a weighted average sensitivity of 89.0% and specificity of 95.2%. Despite using the strictest definition for FF KC, the present study obtained comparable or better results than the single-parameter methods and indices reported in the literature. In some cases, direct comparisons with the literature were not possible because of differences in the compositions and definitions of the study groups, especially the FF KC.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

NASA Astrophysics Data System (ADS)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Lightweight MgB2 superconducting 10 MW wind generator

NASA Astrophysics Data System (ADS)

Marino, I.; Pujana, A.; Sarmiento, G.; Sanz, S.; Merino, J. M.; Tropeano, M.; Sun, J.; Canosa, T.

2016-02-01

The offshore wind market demands a higher power rate and more reliable turbines in order to optimize capital and operational costs. The state-of-the-art shows that both geared and direct-drive conventional generators are difficult to scale up to 10 MW and beyond due to their huge size and weight. Superconducting direct-drive wind generators are considered a promising solution to achieve lighter weight machines. This work presents an innovative 10 MW 8.1 rpm direct-drive partial superconducting generator using MgB2 wire for the field coils. It has a warm iron rotor configuration with the superconducting coils working at 20 K while the rotor core and the armature are at ambient temperature. A cooling system based on cryocoolers installed in the rotor extracts the heat from the superconducting coils by conduction. The generator's main parameters are compared against a permanent magnet reference machine, showing a significant weight and size reduction. The 10 MW superconducting generator concept will be experimentally validated with a small-scale magnetic machine, which has innovative components such as superconducting coils, modular cryostats and cooling systems, and will have similar size and characteristics as the 10 MW generator.

Harmony search optimization in dimensional accuracy of die sinking EDM process using SS316L stainless steel

NASA Astrophysics Data System (ADS)

Deris, A. M.; Zain, A. M.; Sallehuddin, R.; Sharif, S.

2017-09-01

Electric discharge machine (EDM) is one of the widely used nonconventional machining processes for hard and difficult to machine materials. Due to the large number of machining parameters in EDM and its complicated structural, the selection of the optimal solution of machining parameters for obtaining minimum machining performance is remain as a challenging task to the researchers. This paper proposed experimental investigation and optimization of machining parameters for EDM process on stainless steel 316L work piece using Harmony Search (HS) algorithm. The mathematical model was developed based on regression approach with four input parameters which are pulse on time, peak current, servo voltage and servo speed to the output response which is dimensional accuracy (DA). The optimal result of HS approach was compared with regression analysis and it was found HS gave better result y giving the most minimum DA value compared with regression approach.

Development of Experimental Setup of Metal Rapid Prototyping Machine using Selective Laser Sintering Technique

NASA Astrophysics Data System (ADS)

Patil, S. N.; Mulay, A. V.; Ahuja, B. B.

2018-04-01

Unlike in the traditional manufacturing processes, additive manufacturing as rapid prototyping, allows designers to produce parts that were previously considered too complex to make economically. The shift is taking place from plastic prototype to fully functional metallic parts by direct deposition of metallic powders as produced parts can be directly used for desired purpose. This work is directed towards the development of experimental setup of metal rapid prototyping machine using selective laser sintering and studies the various parameters, which plays important role in the metal rapid prototyping using SLS technique. The machine structure in mainly divided into three main categories namely, (1) Z-movement of bed and table, (2) X-Y movement arrangement for LASER movements and (3) feeder mechanism. Z-movement of bed is controlled by using lead screw, bevel gear pair and stepper motor, which will maintain the accuracy of layer thickness. X-Y movements are controlled using timing belt and stepper motors for precise movements of LASER source. Feeder mechanism is then developed to control uniformity of layer thickness metal powder. Simultaneously, the study is carried out for selection of material. Various types of metal powders can be used for metal RP as Single metal powder, mixture of two metals powder, and combination of metal and polymer powder. Conclusion leads to use of mixture of two metals powder to minimize the problems such as, balling effect and porosity. Developed System can be validated by conducting various experiments on manufactured part to check mechanical and metallurgical properties. After studying the results of these experiments, various process parameters as LASER properties (as power, speed etc.), and material properties (as grain size and structure etc.) will be optimized. This work is mainly focused on the design and development of cost effective experimental setup of metal rapid prototyping using SLS technique which will gives the feel of metal rapid prototyping process and its important parameters.

Minimization of energy and surface roughness of the products machined by milling

NASA Astrophysics Data System (ADS)

Belloufi, A.; Abdelkrim, M.; Bouakba, M.; Rezgui, I.

2017-08-01

Metal cutting represents a large portion in the manufacturing industries, which makes this process the largest consumer of energy. Energy consumption is an indirect source of carbon footprint, we know that CO2 emissions come from the production of energy. Therefore high energy consumption requires a large production, which leads to high cost and a large amount of CO2 emissions. At this day, a lot of researches done on the Metal cutting, but the environmental problems of the processes are rarely discussed. The right selection of cutting parameters is an effective method to reduce energy consumption because of the direct relationship between energy consumption and cutting parameters in machining processes. Therefore, one of the objectives of this research is to propose an optimization strategy suitable for machining processes (milling) to achieve the optimum cutting conditions based on the criterion of the energy consumed during the milling. In this paper the problem of energy consumed in milling is solved by an optimization method chosen. The optimization is done according to the different requirements in the process of roughing and finishing under various technological constraints.

Effects of machining parameters on tool life and its optimization in turning mild steel with brazed carbide cutting tool

NASA Astrophysics Data System (ADS)

Dasgupta, S.; Mukherjee, S.

2016-09-01

One of the most significant factors in metal cutting is tool life. In this research work, the effects of machining parameters on tool under wet machining environment were studied. Tool life characteristics of brazed carbide cutting tool machined against mild steel and optimization of machining parameters based on Taguchi design of experiments were examined. The experiments were conducted using three factors, spindle speed, feed rate and depth of cut each having three levels. Nine experiments were performed on a high speed semi-automatic precision central lathe. ANOVA was used to determine the level of importance of the machining parameters on tool life. The optimum machining parameter combination was obtained by the analysis of S/N ratio. A mathematical model based on multiple regression analysis was developed to predict the tool life. Taguchi's orthogonal array analysis revealed the optimal combination of parameters at lower levels of spindle speed, feed rate and depth of cut which are 550 rpm, 0.2 mm/rev and 0.5mm respectively. The Main Effects plot reiterated the same. The variation of tool life with different process parameters has been plotted. Feed rate has the most significant effect on tool life followed by spindle speed and depth of cut.

Modeling of Geometric Error in Linear Guide Way to Improved the vertical three-axis CNC Milling machine’s accuracy

NASA Astrophysics Data System (ADS)

Kwintarini, Widiyanti; Wibowo, Agung; Arthaya, Bagus M.; Yuwana Martawirya, Yatna

2018-03-01

The purpose of this study was to improve the accuracy of three-axis CNC Milling Vertical engines with a general approach by using mathematical modeling methods of machine tool geometric errors. The inaccuracy of CNC machines can be caused by geometric errors that are an important factor during the manufacturing process and during the assembly phase, and are factors for being able to build machines with high-accuracy. To improve the accuracy of the three-axis vertical milling machine, by knowing geometric errors and identifying the error position parameters in the machine tool by arranging the mathematical modeling. The geometric error in the machine tool consists of twenty-one error parameters consisting of nine linear error parameters, nine angle error parameters and three perpendicular error parameters. The mathematical modeling approach of geometric error with the calculated alignment error and angle error in the supporting components of the machine motion is linear guide way and linear motion. The purpose of using this mathematical modeling approach is the identification of geometric errors that can be helpful as reference during the design, assembly and maintenance stages to improve the accuracy of CNC machines. Mathematically modeling geometric errors in CNC machine tools can illustrate the relationship between alignment error, position and angle on a linear guide way of three-axis vertical milling machines.

Selection of Wire Electrical Discharge Machining Process Parameters on Stainless Steel AISI Grade-304 using Design of Experiments Approach

NASA Astrophysics Data System (ADS)

Lingadurai, K.; Nagasivamuni, B.; Muthu Kamatchi, M.; Palavesam, J.

2012-06-01

Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts of hard materials with complex shapes. Parts having sharp edges that pose difficulties to be machined by the main stream machining processes can be easily machined by WEDM process. Design of Experiments approach (DOE) has been reported in this work for stainless steel AISI grade-304 which is used in cryogenic vessels, evaporators, hospital surgical equipment, marine equipment, fasteners, nuclear vessels, feed water tubing, valves, refrigeration equipment, etc., is machined by WEDM with brass wire electrode. The DOE method is used to formulate the experimental layout, to analyze the effect of each parameter on the machining characteristics, and to predict the optimal choice for each WEDM parameter such as voltage, pulse ON, pulse OFF and wire feed. It is found that these parameters have a significant influence on machining characteristic such as metal removal rate (MRR), kerf width and surface roughness (SR). The analysis of the DOE reveals that, in general the pulse ON time significantly affects the kerf width and the wire feed rate affects SR, while, the input voltage mainly affects the MRR.

The first gravitational-wave burst GW150914, as predicted by the scenario machine

NASA Astrophysics Data System (ADS)

Lipunov, V. M.; Kornilov, V.; Gorbovskoy, E.; Tiurina, N.; Balanutsa, P.; Kuznetsov, A.

2017-02-01

The Advanced LIGO observatory recently reported (Abbott et al., 2016a) the first direct detection of gravitational waves predicted by Einstein (1916). The detection of this event was predicted in 1997 on the basis of the Scenario Machine population synthesis calculations (Lipunov et al., 1997b) Now we discuss the parameters of binary black holes and event rates predicted by different scenarios of binary evolution. We give a simple explanation of the big difference between detected black hole masses and the mean black hole masses observed in of X-ray Nova systems. The proximity of the masses of the components of GW150914 is in good agreement with the observed initial mass ratio distribution in massive binary systems, as is used in Scenario Machine calculations for massive binaries.

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

The Impact Of Surface Shape Of Chip-Breaker On Machined Surface

NASA Astrophysics Data System (ADS)

Šajgalík, Michal; Czán, Andrej; Martinček, Juraj; Varga, Daniel; Hemžský, Pavel; Pitela, David

2015-12-01

Machined surface is one of the most used indicators of workpiece quality. But machined surface is influenced by several factors such as cutting parameters, cutting material, shape of cutting tool or cutting insert, micro-structure of machined material and other known as technological parameters. By improving of these parameters, we can improve machined surface. In the machining, there is important to identify the characteristics of main product of these processes - workpiece, but also the byproduct - the chip. Size and shape of chip has impact on lifetime of cutting tools and its inappropriate form can influence the machine functionality and lifetime, too. This article deals with elimination of long chip created when machining of shaft in automotive industry and with impact of shape of chip-breaker on shape of chip in various cutting conditions based on production requirements.

A modeling of elementary passes taking into account the firing angle in abrasive water jet machining of titanium alloy

NASA Astrophysics Data System (ADS)

Bui, Van-Hung; Gilles, Patrick; Cohen, Guillaume; Rubio, Walter

2018-05-01

The use of titanium alloys in the aeronautical and high technology domains is widespread. The high strength and the low mass are two outstanding characteristics of titanium alloys which permit to produce parts for these domains. As other hard materials, it is challenging to generate 3D surfaces (e.g. pockets) when using conventional cutting methods. The development of Abrasive Water Jet Machining (AWJM) technology shows the capability to cut any kind of materials and it seems to be a good solution for such titanium materials with low specific force, low deformation of parts and low thermal shocks. Applying this technology for generating 3D surfaces requires to adopt a modelling approach. However, a general methodology results in complex models due to a lot of parameters of the machining process and based on numerous experiments. This study introduces an extended geometry model of an elementary pass when changing the firing angle during machining Ti-6AL-4V titanium alloy with a given machine configuration. Several experiments are conducted to observe the influence of major kinematic operating parameters, i.e. jet inclination angle (α) (perpendicular to the feed direction) and traverse speed (Vf). The material exposure time and the erosion capability of abrasives particles are affected directly by a variation of the traverse speed (Vf) and firing angle (α). These variations lead to different erosion rates along the kerf profile characterized by the depth and width of cut. A comparison demonstrated an efficiency of the proposed model for depth and width of elementary passes. Based on knowledge of the influence of both firing angle and traverse speed on the elementary pass shape, the proposed model allows to develop the simulation of AWJM process and paves a way for milling flat bottom pockets and 3D complex shapes.

Dark matter effective field theory scattering in direct detection experiments

DOE PAGES

Schneck, K.

2015-05-01

We examine the consequences of the effective field theory (EFT) of dark matter–nucleon scattering for current and proposed direct detection experiments. Exclusion limits on EFT coupling constants computed using the optimum interval method are presented for SuperCDMS Soudan, CDMS II, and LUX, and the necessity of combining results from multiple experiments in order to determine dark matter parameters is discussed. We demonstrate that spectral differences between the standard dark matter model and a general EFT interaction can produce a bias when calculating exclusion limits and when developing signal models for likelihood and machine learning techniques. We also discuss the implicationsmore » of the EFT for the next-generation (G2) direct detection experiments and point out regions of complementarity in the EFT parameter space.« less

Dark matter effective field theory scattering in direct detection experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schneck, K.; Cabrera, B.; Cerdeño, D. G.

2015-05-18

We examine the consequences of the effective field theory (EFT) of dark matter-nucleon scattering for current and proposed direct detection experiments. Exclusion limits on EFT coupling constants computed using the optimum interval method are presented for SuperCDMS Soudan, CDMS II, and LUX, and the necessity of combining results from multiple experiments in order to determine dark matter parameters is discussed. Here. we demonstrate that spectral differences between the standard dark matter model and a general EFT interaction can produce a bias when calculating exclusion limits and when developing signal models for likelihood and machine learning techniques. In conclusion, we discussmore » the implications of the EFT for the next-generation (G2) direct detection experiments and point out regions of complementarity in the EFT parameter space.« less

Dark matter effective field theory scattering in direct detection experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schneck, K.; Cabrera, B.; Cerdeño, D. G.

2015-05-18

We examine the consequences of the effective field theory (EFT) of dark matter–nucleon scattering for current and proposed direct detection experiments. Exclusion limits on EFT coupling constants computed using the optimum interval method are presented for SuperCDMS Soudan, CDMS II, and LUX, and the necessity of combining results from multiple experiments in order to determine dark matter parameters is discussed. We demonstrate that spectral differences between the standard dark matter model and a general EFT interaction can produce a bias when calculating exclusion limits and when developing signal models for likelihood and machine learning techniques. We also discuss the implicationsmore » of the EFT for the next-generation (G2) direct detection experiments and point out regions of complementarity in the EFT parameter space.« less

Automated Design of Complex Dynamic Systems

PubMed Central

Hermans, Michiel; Schrauwen, Benjamin; Bienstman, Peter; Dambre, Joni

2014-01-01

Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems. PMID:24497969

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin

2017-09-01

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

3D FE simulation of semi-finishing machining of Ti6Al4V additively manufactured by direct metal laser sintering

NASA Astrophysics Data System (ADS)

Imbrogno, Stano; Rinaldi, Sergio; Raso, Antonio; Bordin, Alberto; Bruschi, Stefania; Umbrello, Domenico

2018-05-01

The Additive Manufacturing techniques are gaining more and more interest in various industrial fields due to the possibility of drastically reduce the material waste during the production processes, revolutionizing the standard scheme and strategies of the manufacturing processes. However, the metal parts shape produced, frequently do not satisfy the tolerances as well as the surface quality requirements. During the design phase, the finite element simulation results a fundamental tool to help the engineers in the correct decision of the most suitable process parameters, especially in manufacturing processes, in order to produce products of high quality. The aim of this work is to develop a 3D finite element model of semi-finishing turning operation of Ti6Al4V, produced via Direct Metal Laser Sintering (DMLS). A customized user sub-routine was built-up in order to model the mechanical behavior of the material under machining operations to predict the main fundamental variables as cutting forces and temperature. Moreover, the machining induced alterations are also studied by the finite element model developed.

Advancing three-dimensional MEMS by complimentary laser micro manufacturing

NASA Astrophysics Data System (ADS)

Palmer, Jeremy A.; Williams, John D.; Lemp, Tom; Lehecka, Tom M.; Medina, Francisco; Wicker, Ryan B.

2006-01-01

This paper describes improvements that enable engineers to create three-dimensional MEMS in a variety of materials. It also provides a means for selectively adding three-dimensional, high aspect ratio features to pre-existing PMMA micro molds for subsequent LIGA processing. This complimentary method involves in situ construction of three-dimensional micro molds in a stand-alone configuration or directly adjacent to features formed by x-ray lithography. Three-dimensional micro molds are created by micro stereolithography (MSL), an additive rapid prototyping technology. Alternatively, three-dimensional features may be added by direct femtosecond laser micro machining. Parameters for optimal femtosecond laser micro machining of PMMA at 800 nanometers are presented. The technical discussion also includes strategies for enhancements in the context of material selection and post-process surface finish. This approach may lead to practical, cost-effective 3-D MEMS with the surface finish and throughput advantages of x-ray lithography. Accurate three-dimensional metal microstructures are demonstrated. Challenges remain in process planning for micro stereolithography and development of buried features following femtosecond laser micro machining.

Product design for energy reduction in concurrent engineering: An Inverted Pyramid Approach

NASA Astrophysics Data System (ADS)

Alkadi, Nasr M.

Energy factors in product design in concurrent engineering (CE) are becoming an emerging dimension for several reasons; (a) the rising interest in "green design and manufacturing", (b) the national energy security concerns and the dramatic increase in energy prices, (c) the global competition in the marketplace and global climate change commitments including carbon tax and emission trading systems, and (d) the widespread recognition of the need for sustainable development. This research presents a methodology for the intervention of energy factors in concurrent engineering product development process to significantly reduce the manufacturing energy requirement. The work presented here is the first attempt at integrating the design for energy in concurrent engineering framework. It adds an important tool to the DFX toolbox for evaluation of the impact of design decisions on the product manufacturing energy requirement early during the design phase. The research hypothesis states that "Product Manufacturing Energy Requirement is a Function of Design Parameters". The hypothesis was tested by conducting experimental work in machining and heat treating that took place at the manufacturing lab of the Industrial and Management Systems Engineering Department (IMSE) at West Virginia University (WVU) and at a major U.S steel manufacturing plant, respectively. The objective of the machining experiment was to study the effect of changing specific product design parameters (Material type and diameter) and process design parameters (metal removal rate) on a gear head lathe input power requirement through performing defined sets of machining experiments. The objective of the heat treating experiment was to study the effect of varying product charging temperature on the fuel consumption of a walking beams reheat furnace. The experimental work in both directions have revealed important insights into energy utilization in machining and heat-treating processes and its variance based on product, process, and system design parameters. In depth evaluation to how the design and manufacturing normally happen in concurrent engineering provided a framework to develop energy system levels in machining within the concurrent engineering environment using the method of "Inverted Pyramid Approach", (IPA). The IPA features varying levels of output energy based information depending on the input design parameters that is available during each stage (level) of the product design. The experimental work, the in-depth evaluation of design and manufacturing in CE, and the developed energy system levels in machining provided a solid base for the development of the model for the design for energy reduction in CE. The model was used to analyze an example part where 12 evolving designs were thoroughly reviewed to investigate the sensitivity of energy to design parameters in machining. The model allowed product design teams to address manufacturing energy concerns early during the design stage. As a result, ranges for energy sensitive design parameters impacting product manufacturing energy consumption were found in earlier levels. As designer proceeds to deeper levels in the model, this range tightens and results in significant energy reductions.

Characterisation of soft magnetic materials by measurement: Evaluation of uncertainties up to 1.8 T and 9 kHz

NASA Astrophysics Data System (ADS)

Elfgen, S.; Franck, D.; Hameyer, K.

2018-04-01

Magnetic measurements are indispensable for the characterization of soft magnetic material used e.g. in electrical machines. Characteristic values are used as quality control during production and for the parametrization of material models. Uncertainties and errors in the measurements are reflected directly in the parameters of the material models. This can result in over-dimensioning and inaccuracies in simulations for the design of electrical machines. Therefore, existing influencing factors in the characterization of soft magnetic materials are named and their resulting uncertainties contributions studied. The analysis of the resulting uncertainty contributions can serve the operator as additional selection criteria for different measuring sensors. The investigation is performed for measurements within and outside the currently prescribed standard, using a Single sheet tester and its impact on the identification of iron loss parameter is studied.

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

An online brain-machine interface using decoding of movement direction from the human electrocorticogram

NASA Astrophysics Data System (ADS)

Milekovic, Tomislav; Fischer, Jörg; Pistohl, Tobias; Ruescher, Johanna; Schulze-Bonhage, Andreas; Aertsen, Ad; Rickert, Jörn; Ball, Tonio; Mehring, Carsten

2012-08-01

A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part.

PubMed

Ahmed, Yassmin Seid; Fox-Rabinovich, German; Paiva, Jose Mario; Wagg, Terry; Veldhuis, Stephen Clarence

2017-10-25

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool-chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part

PubMed Central

Fox-Rabinovich, German; Wagg, Terry

2017-01-01

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool–chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear. PMID:29068405

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

30 CFR 75.703 - Grounding offtrack direct-current machines and the enclosures of related detached components.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Grounding offtrack direct-current machines and...-UNDERGROUND COAL MINES Grounding § 75.703 Grounding offtrack direct-current machines and the enclosures of related detached components. [Statutory Provisions] The frames of all offtrack direct-current machines and...

30 CFR 75.703 - Grounding offtrack direct-current machines and the enclosures of related detached components.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Grounding offtrack direct-current machines and...-UNDERGROUND COAL MINES Grounding § 75.703 Grounding offtrack direct-current machines and the enclosures of related detached components. [Statutory Provisions] The frames of all offtrack direct-current machines and...

Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

DOE PAGES

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

2014-12-18

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Investigations on high speed machining of EN-353 steel alloy under different machining environments

NASA Astrophysics Data System (ADS)

Venkata Vishnu, A.; Jamaleswara Kumar, P.

2018-03-01

The addition of Nano Particles into conventional cutting fluids enhances its cooling capabilities; in the present paper an attempt is made by adding nano sized particles into conventional cutting fluids. Taguchi Robust Design Methodology is employed in order to study the performance characteristics of different turning parameters i.e. cutting speed, feed rate, depth of cut and type of tool under different machining environments i.e. dry machining, machining with lubricant - SAE 40 and machining with mixture of nano sized particles of Boric acid and base fluid SAE 40. A series of turning operations were performed using L27 (3)13 orthogonal array, considering high cutting speeds and the other machining parameters to measure hardness. The results are compared among the different machining environments, and it is concluded that there is considerable improvement in the machining performance using lubricant SAE 40 and mixture of SAE 40 + boric acid compared with dry machining. The ANOVA suggests that the selected parameters and the interactions are significant and cutting speed has most significant effect on hardness.

Virtual screening of inorganic materials synthesis parameters with deep learning

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Kevin; Jegelka, Stefanie; Olivetti, Elsa

2017-12-01

Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, high-dimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO3 synthesis parameter sets, propose driving factors for brookite TiO2 formation, and identify correlations between alkali-ion intercalation and MnO2 polymorph selection.

Modifications of Ti-6Al-4V surfaces by direct-write laser machining of linear grooves

NASA Astrophysics Data System (ADS)

Ulerich, Joseph P.; Ionescu, Lara C.; Chen, Jianbo; Soboyejo, Winston O.; Arnold, Craig B.

2007-02-01

As patients who receive orthopedic implants live longer and opt for surgery at a younger age, the need to extend the in vivo lifetimes of these implants has grown. One approach is to pattern implant surfaces with linear grooves, which elicit a cellular response known as contact guidance. Lasers provide a unique method of generating these surface patterns because they are capable of modifying physical and chemical properties over multiple length scales. In this paper we explore the relationship between surface morphology and laser parameters such as fluence, pulse overlap (translation distance), number of passes, and machining environment. We find that using simple procedures involving multiple passes it is possible to manipulate groove properties such as depth, shape, sub-micron roughness, and chemical composition of the Ti-6Al-4V oxide layer. Finally, we demonstrate this procedure by machining several sets of grooves with the same primary groove parameters but varied secondary characteristics. The significance of the secondary groove characteristics is demonstrated by preliminary cell studies indicating that the grooves exhibit basic features of contact guidance and that the cell proliferation in these grooves are significantly altered despite their similar primary characteristics. With further study it will be possible to use specific laser parameters during groove formation to create optimal physical and chemical properties for improved osseointegration.

Multi-objective optimization model of CNC machining to minimize processing time and environmental impact

NASA Astrophysics Data System (ADS)

Hamada, Aulia; Rosyidi, Cucuk Nur; Jauhari, Wakhid Ahmad

2017-11-01

Minimizing processing time in a production system can increase the efficiency of a manufacturing company. Processing time are influenced by application of modern technology and machining parameter. Application of modern technology can be apply by use of CNC machining, one of the machining process can be done with a CNC machining is turning. However, the machining parameters not only affect the processing time but also affect the environmental impact. Hence, optimization model is needed to optimize the machining parameters to minimize the processing time and environmental impact. This research developed a multi-objective optimization to minimize the processing time and environmental impact in CNC turning process which will result in optimal decision variables of cutting speed and feed rate. Environmental impact is converted from environmental burden through the use of eco-indicator 99. The model were solved by using OptQuest optimization software from Oracle Crystal Ball.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data

NASA Astrophysics Data System (ADS)

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-01

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data.

PubMed

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-07

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Predictive Modeling and Optimization of Vibration-assisted AFM Tip-based Nanomachining

NASA Astrophysics Data System (ADS)

Kong, Xiangcheng

The tip-based vibration-assisted nanomachining process offers a low-cost, low-effort technique in fabricating nanometer scale 2D/3D structures in sub-100 nm regime. To understand its mechanism, as well as provide the guidelines for process planning and optimization, we have systematically studied this nanomachining technique in this work. To understand the mechanism of this nanomachining technique, we firstly analyzed the interaction between the AFM tip and the workpiece surface during the machining process. A 3D voxel-based numerical algorithm has been developed to calculate the material removal rate as well as the contact area between the AFM tip and the workpiece surface. As a critical factor to understand the mechanism of this nanomachining process, the cutting force has been analyzed and modeled. A semi-empirical model has been proposed by correlating the cutting force with the material removal rate, which was validated using experimental data from different machining conditions. With the understanding of its mechanism, we have developed guidelines for process planning of this nanomachining technique. To provide the guideline for parameter selection, the effect of machining parameters on the feature dimensions (depth and width) has been analyzed. Based on ANOVA test results, the feature width is only controlled by the XY vibration amplitude, while the feature depth is affected by several machining parameters such as setpoint force and feed rate. A semi-empirical model was first proposed to predict the machined feature depth under given machining condition. Then, to reduce the computation intensity, linear and nonlinear regression models were also proposed and validated using experimental data. Given the desired feature dimensions, feasible machining parameters could be provided using these predictive feature dimension models. As the tip wear is unavoidable during the machining process, the machining precision will gradually decrease. To maintain the machining quality, the guideline for when to change the tip should be provided. In this study, we have developed several metrics to detect tip wear, such as tip radius and the pull-off force. The effect of machining parameters on the tip wear rate has been studied using these metrics, and the machining distance before a tip must be changed has been modeled using these machining parameters. Finally, the optimization functions have been built for unit production time and unit production cost subject to realistic constraints, and the optimal machining parameters can be found by solving these functions.

Efficient machining of ultra precise steel moulds with freeform surfaces

NASA Astrophysics Data System (ADS)

Bulla, B.; Robertson, D. J.; Dambon, O.; Klocke, F.

2013-09-01

Ultra precision diamond turning of hardened steel to produce optical quality surfaces can be realized by applying an ultrasonic assisted process. With this technology optical moulds used typically for injection moulding can be machined directly from steel without the requirement to overcoat the mould with a diamond machinable material such as Nickel Phosphor. This has both the advantage of increasing the mould tool lifetime and also reducing manufacture costs by dispensing with the relatively expensive plating process. This publication will present results we have obtained for generating free form moulds in hardened steel by means of ultrasonic assisted diamond turning with a vibration frequency of 80 kHz. To provide a baseline with which to characterize the system performance we perform plane cutting experiments on different steel alloys with different compositions. The baseline machining results provides us information on the surface roughness and on tool wear caused during machining and we relate these to material composition. Moving on to freeform surfaces, we will present a theoretical background to define the machine program parameters for generating free forms by applying slow slide servo machining techniques. A solution for optimal part generation is introduced which forms the basis for the freeform machining experiments. The entire process chain, from the raw material through to ultra precision machining is presented, with emphasis on maintaining surface alignment when moving a component from CNC pre-machining to final machining using ultrasonic assisted diamond turning. The free form moulds are qualified on the basis of the surface roughness measurements and a form error map comparing the machined surface with the originally defined surface. These experiments demonstrate the feasibility of efficient free form machining applying ultrasonic assisted diamond turning of hardened steel.

Design and milling manufacture of polyurethane custom contoured cushions for wheelchair users.

PubMed

da Silva, Fabio Pinto; Beretta, Elisa Marangon; Prestes, Rafael Cavalli; Kindlein Junior, Wilson

2011-01-01

The design of custom contoured cushions manufactured in flexible polyurethane foams is an option to improve positioning and comfort for people with disabilities that spend most of the day seated in the same position. These surfaces increase the contact area between the seat and the user. This fact contributes to minimise the local pressures that can generate problems like decubitus ulcers. The present research aims at establishing development routes for custom cushion production to wheelchair users. This study also contributes to the investigation of Computer Numerical Control (CNC) machining of flexible polyurethane foams. The proposed route to obtain the customised seat began with acquiring the user's contour in adequate posture through plaster cast. To collect the surface geometry, the cast was three-dimensionally scanned and manipulated in CAD/CAM software. CNC milling parameters such as tools, spindle speeds and feed rates to machine flexible polyurethane foams were tested. These parameters were analysed regarding the surface quality. The best parameters were then tested in a customised seat. The possible dimensional changes generated during foam cutting were analysed through 3D scanning. Also, the customised seat pressure and temperature distribution was tested. The best parameters found for foams with a density of 50kg/cm(3) were high spindle speeds (24000 rpm) and feed rates between 2400-4000mm/min. Those parameters did not generate significant deformities in the machined cushions. The custom contoured cushion satisfactorily increased the contact area between wheelchair and user, as it distributed pressure and heat evenly. Through this study it was possible to define routes for the development and manufacturing of customised seats using direct CNC milling in flexible polyurethane foams. It also showed that custom contoured cushions efficiently distribute pressure and temperature, which is believed to minimise tissue lesions such as pressure ulcers.

SU-E-T-113: Dose Distribution Using Respiratory Signals and Machine Parameters During Treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imae, T; Haga, A; Saotome, N

Purpose: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. Treatment plans for lung tumors using stereotactic body radiotherapy (SBRT) are calculated with planning computed tomography (CT) images only exhale phase. Purpose of this study is to evaluate dose distribution by reconstructing from only the data such as respiratory signals and machine parameters acquired during treatment. Methods: Phantom and three patients with lung tumor underwent CT scans for treatment planning. They were treated by VMAT while acquiring projection images to derive their respiratory signals and machine parameters including positions ofmore » multi leaf collimators, dose rates and integrated monitor units. The respiratory signals were divided into 4 and 10 phases and machine parameters were correlated with the divided respiratory signals based on the gantry angle. Dose distributions of each respiratory phase were calculated from plans which were reconstructed from the respiratory signals and the machine parameters during treatment. The doses at isocenter, maximum point and the centroid of target were evaluated. Results and Discussion: Dose distributions during treatment were calculated using the machine parameters and the respiratory signals detected from projection images. Maximum dose difference between plan and in treatment distribution was −1.8±0.4% at centroid of target and dose differences of evaluated points between 4 and 10 phases were no significant. Conclusion: The present method successfully evaluated dose distribution using respiratory signals and machine parameters during treatment. This method is feasible to verify the actual dose for moving target.« less

Channel Efficiency with Security Enhancement for Remote Condition Monitoring of Multi Machine System Using Hybrid Huffman Coding

NASA Astrophysics Data System (ADS)

Datta, Jinia; Chowdhuri, Sumana; Bera, Jitendranath

2016-12-01

This paper presents a novel scheme of remote condition monitoring of multi machine system where a secured and coded data of induction machine with different parameters is communicated between a state-of-the-art dedicated hardware Units (DHU) installed at the machine terminal and a centralized PC based machine data management (MDM) software. The DHUs are built for acquisition of different parameters from the respective machines, and hence are placed at their nearby panels in order to acquire different parameters cost effectively during their running condition. The MDM software collects these data through a communication channel where all the DHUs are networked using RS485 protocol. Before transmitting, the parameter's related data is modified with the adoption of differential pulse coded modulation (DPCM) and Huffman coding technique. It is further encrypted with a private key where different keys are used for different DHUs. In this way a data security scheme is adopted during its passage through the communication channel in order to avoid any third party attack into the channel. The hybrid mode of DPCM and Huffman coding is chosen to reduce the data packet length. A MATLAB based simulation and its practical implementation using DHUs at three machine terminals (one healthy three phase, one healthy single phase and one faulty three phase machine) proves its efficacy and usefulness for condition based maintenance of multi machine system. The data at the central control room are decrypted and decoded using MDM software. In this work it is observed that Chanel efficiency with respect to different parameter measurements has been increased very much.

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

NASA Astrophysics Data System (ADS)

Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

2017-08-01

We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

A comparative study of electrochemical machining process parameters by using GA and Taguchi method

NASA Astrophysics Data System (ADS)

Soni, S. K.; Thomas, B.

2017-11-01

In electrochemical machining quality of machined surface strongly depend on the selection of optimal parameter settings. This work deals with the application of Taguchi method and genetic algorithm using MATLAB to maximize the metal removal rate and minimize the surface roughness and overcut. In this paper a comparative study is presented for drilling of LM6 AL/B4C composites by comparing the significant impact of numerous machining process parameters such as, electrolyte concentration (g/l),machining voltage (v),frequency (hz) on the response parameters (surface roughness, material removal rate and over cut). Taguchi L27 orthogonal array was chosen in Minitab 17 software, for the investigation of experimental results and also multiobjective optimization done by genetic algorithm is employed by using MATLAB. After obtaining optimized results from Taguchi method and genetic algorithm, a comparative results are presented.

Friction Laws Derived From the Acoustic Emissions of a Laboratory Fault by Machine Learning

NASA Astrophysics Data System (ADS)

Rouet-Leduc, B.; Hulbert, C.; Ren, C. X.; Bolton, D. C.; Marone, C.; Johnson, P. A.

2017-12-01

Fault friction controls nearly all aspects of fault rupture, yet it is only possible to measure in the laboratory. Here we describe laboratory experiments where acoustic emissions are recorded from the fault. We find that by applying a machine learning approach known as "extreme gradient boosting trees" to the continuous acoustical signal, the fault friction can be directly inferred, showing that instantaneous characteristics of the acoustic signal are a fingerprint of the frictional state. This machine learning-based inference leads to a simple law that links the acoustic signal to the friction state, and holds for every stress cycle the laboratory fault goes through. The approach does not use any other measured parameter than instantaneous statistics of the acoustic signal. This finding may have importance for inferring frictional characteristics from seismic waves in Earth where fault friction cannot be measured.

Modeling and simulation of five-axis virtual machine based on NX

NASA Astrophysics Data System (ADS)

Li, Xiaoda; Zhan, Xianghui

2018-04-01

Virtual technology in the machinery manufacturing industry has shown the role of growing. In this paper, the Siemens NX software is used to model the virtual CNC machine tool, and the parameters of the virtual machine are defined according to the actual parameters of the machine tool so that the virtual simulation can be carried out without loss of the accuracy of the simulation. How to use the machine builder of the CAM module to define the kinematic chain and machine components of the machine is described. The simulation of virtual machine can provide alarm information of tool collision and over cutting during the process to users, and can evaluate and forecast the rationality of the technological process.

Optimization of process parameters in drilling of fibre hybrid composite using Taguchi and grey relational analysis

NASA Astrophysics Data System (ADS)

Vijaya Ramnath, B.; Sharavanan, S.; Jeykrishnan, J.

2017-03-01

Nowadays quality plays a vital role in all the products. Hence, the development in manufacturing process focuses on the fabrication of composite with high dimensional accuracy and also incurring low manufacturing cost. In this work, an investigation on machining parameters has been performed on jute-flax hybrid composite. Here, the two important responses characteristics like surface roughness and material removal rate are optimized by employing 3 machining input parameters. The input variables considered are drill bit diameter, spindle speed and feed rate. Machining is done on CNC vertical drilling machine at different levels of drilling parameters. Taguchi’s L16 orthogonal array is used for optimizing individual tool parameters. Analysis Of Variance is used to find the significance of individual parameters. The simultaneous optimization of the process parameters is done by grey relational analysis. The results of this investigation shows that, spindle speed and drill bit diameter have most effect on material removal rate and surface roughness followed by feed rate.

Trends and developments in industrial machine vision: 2013

NASA Astrophysics Data System (ADS)

Niel, Kurt; Heinzl, Christoph

2014-03-01

When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own contribution.

Comparison of effects of overload on parameters and performance of samarium-cobalt and strontium-ferrite radially oriented permanent magnet brushless DC motors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Demerdash, N.A.; Nehl, T.W.; Nyamusa, T.A.

1985-08-01

Effects of high momentary overloads on the samarium-cobalt and strontium-ferrite permanent magnets and the magnetic field in electronically commutated brushless dc machines, as well as their impact on the associated machine parameters were studied. The effect of overload on the machine parameters, and subsequently on the machine system performance was also investigated. This was accomplished through the combined use of finite element analysis of the magnetic field in such machines, perturbation of the magnetic energies to determine machine inductances, and dynamic simulation of the performance of brushless dc machines, when energized from voltage source inverters. These effects were investigated throughmore » application of the above methods to two equivalent 15 hp brushless dc motors, one of which was built with samarium-cobalt magnets, while the other was built with strontium- ferrite magnets. For momentary overloads as high as 4.5 p.u. magnet flux reductions of 29% and 42% of the no load flux were obtained in the samarium-cobalt and strontiumferrite machines, respectively. Corresponding reductions in the line to line armature inductances of 52% and 46% of the no load values were reported for the samarium-cobalt and strontium-ferrite cases, respectively. The overload affected the profiles and magnitudes of armature induced back emfs. Subsequently, the effects of overload on machine parameters were found to have significant impact on the performance of the machine systems, where findings indicate that the samarium-cobalt unit is more suited for higher overload duties than the strontium-ferrite machine.« less

Analysis and optimization of machining parameters of laser cutting for polypropylene composite

NASA Astrophysics Data System (ADS)

Deepa, A.; Padmanabhan, K.; Kuppan, P.

2017-11-01

Present works explains about machining of self-reinforced Polypropylene composite fabricated using hot compaction method. The objective of the experiment is to find optimum machining parameters for Polypropylene (PP). Laser power and Machining speed were the parameters considered in response to tensile test and Flexure test. Taguchi method is used for experimentation. Grey Relational Analysis (GRA) is used for multiple process parameter optimization. ANOVA (Analysis of Variance) is used to find impact for process parameter. Polypropylene has got the great application in various fields like, it is used in the form of foam in model aircraft and other radio-controlled vehicles, thin sheets (∼2-20μm) used as a dielectric, PP is also used in piping system, it is also been used in hernia and pelvic organ repair or protect new herrnis in the same location.

Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology

NASA Astrophysics Data System (ADS)

Kumar, Amit; Soota, Tarun; Kumar, Jitendra

2018-03-01

Wire electric discharge machining (WEDM) is one of the advanced machining processes. Response surface methodology coupled with Grey relation analysis method has been proposed and used to optimise the machining parameters of WEDM. A face centred cubic design is used for conducting experiments on high speed steel (HSS) M2 grade workpiece material. The regression model of significant factors such as pulse-on time, pulse-off time, peak current, and wire feed is considered for optimising the responses variables material removal rate (MRR), surface roughness and Kerf width. The optimal condition of the machining parameter was obtained using the Grey relation grade. ANOVA is applied to determine significance of the input parameters for optimising the Grey relation grade.

Machining of bone: Analysis of cutting force and surface roughness by turning process.

PubMed

Noordin, M Y; Jiawkok, N; Ndaruhadi, P Y M W; Kurniawan, D

2015-11-01

There are millions of orthopedic surgeries and dental implantation procedures performed every year globally. Most of them involve machining of bones and cartilage. However, theoretical and analytical study on bone machining is lagging behind its practice and implementation. This study views bone machining as a machining process with bovine bone as the workpiece material. Turning process which makes the basis of the actually used drilling process was experimented. The focus is on evaluating the effects of three machining parameters, that is, cutting speed, feed, and depth of cut, to machining responses, that is, cutting forces and surface roughness resulted by the turning process. Response surface methodology was used to quantify the relation between the machining parameters and the machining responses. The turning process was done at various cutting speeds (29-156 m/min), depths of cut (0.03 -0.37 mm), and feeds (0.023-0.11 mm/rev). Empirical models of the resulted cutting force and surface roughness as the functions of cutting speed, depth of cut, and feed were developed. Observation using the developed empirical models found that within the range of machining parameters evaluated, the most influential machining parameter to the cutting force is depth of cut, followed by feed and cutting speed. The lowest cutting force was obtained at the lowest cutting speed, lowest depth of cut, and highest feed setting. For surface roughness, feed is the most significant machining condition, followed by cutting speed, and with depth of cut showed no effect. The finest surface finish was obtained at the lowest cutting speed and feed setting. © IMechE 2015.

Design and analysis of drum lathe for manufacturing large-scale optical microstructured surface and load characteristics of aerostatic spindle

NASA Astrophysics Data System (ADS)

Wu, Dongxu; Qiao, Zheng; Wang, Bo; Wang, Huiming; Li, Guo

2014-08-01

In this paper, a four-axis ultra-precision lathe for machining large-scale drum mould with microstructured surface is presented. Firstly, because of the large dimension and weight of drum workpiece, as well as high requirement of machining accuracy, the design guidelines and component parts of this drum lathe is introduced in detail, including control system, moving and driving components, position feedback system and so on. Additionally, the weight of drum workpiece would result in the structural deformation of this lathe, therefore, this paper analyses the effect of structural deformation on machining accuracy by means of ANSYS. The position change is approximately 16.9nm in the X-direction(sensitive direction) which could be negligible. Finally, in order to study the impact of bearing parameters on the load characteristics of aerostatic journal bearing, one of the famous computational fluid dynamics(CFD) software, FLUENT, is adopted, and a series of simulations are carried out. The result shows that the aerostatic spindle has superior performance of carrying capacity and stiffness, it is possible for this lathe to bear the weight of drum workpiece up to 1000kg since there are two aerostatic spindles in the headstock and tailstock.

Performance analysis of a new radial-axial flux machine with SMC cores and ferrite magnets

NASA Astrophysics Data System (ADS)

Liu, Chengcheng; Wang, Youhua; Lei, Gang; Guo, Youguang; Zhu, Jianguo

2017-05-01

Soft magnetic composite (SMC) is a popular material in designing of new 3D flux electrical machines nowadays for it has the merits of isotropic magnetic characteristic, low eddy current loss and high design flexibility over the electric steel. The axial flux machine (AFM) with the extended stator tooth tip both in the radial and circumferential direction is a good example, which has been investigated in the last years. Based on the 3D flux AFM and radial flux machine, this paper proposes a new radial-axial flux machine (RAFM) with SMC cores and ferrite magnets, which has very high torque density though the low cost low magnetic energy ferrite magnet is utilized. Moreover, the cost of RAFM is quite low since the manufacturing cost can be reduced by using the SMC cores and the material cost will be decreased due to the adoption of the ferrite magnets. The 3D finite element method (FEM) is used to calculate the magnetic flux density distribution and electromagnetic parameters. For the core loss calculation, the rotational core loss computation method is used based on the experiment results from previous 3D magnetic tester.

Recent developments in turning hardened steels - A review

NASA Astrophysics Data System (ADS)

Sivaraman, V.; Prakash, S.

2017-05-01

Hard materials ranging from HRC 45 - 68 such as hardened AISI H13, AISI 4340, AISI 52100, D2 STL, D3 STEEL Steel etc., need super hard tool materials to machine. Turning of these hard materials is termed as hard turning. Hard turning makes possible direct machining of the hard materials and also eliminates the lubricant requirement and thus favoring dry machining. Hard turning is a finish turning process and hence conventional grinding is not required. Development of the new advanced super hard tool materials such as ceramic inserts, Cubic Boron Nitride, Polycrystalline Cubic Boron Nitride etc. enabled the turning of these materials. PVD and CVD methods of coating have made easier the production of single and multi layered coated tool inserts. Coatings of TiN, TiAlN, TiC, Al2O3, AlCrN over cemented carbide inserts has lead to the machining of difficult to machine materials. Advancement in the process of hard machining paved way for better surface finish, long tool life, reduced tool wear, cutting force and cutting temperatures. Micro and Nano coated carbide inserts, nanocomposite coated PCBN inserts, micro and nano CBN coated carbide inserts and similar developments have made machining of hardened steels much easier and economical. In this paper, broad literature review on turning of hardened steels including optimizing process parameters, cooling requirements, different tool materials etc., are done.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Catalytic aided electrical discharge machining of polycrystalline diamond - parameter analysis of finishing condition

NASA Astrophysics Data System (ADS)

Haikal Ahmad, M. A.; Zulafif Rahim, M.; Fauzi, M. F. Mohd; Abdullah, Aslam; Omar, Z.; Ding, Songlin; Ismail, A. E.; Rasidi Ibrahim, M.

2018-01-01

Polycrystalline diamond (PCD) is regarded as among the hardest material in the world. Electrical Discharge Machining (EDM) typically used to machine this material because of its non-contact process nature. This investigation was purposely done to compare the EDM performances of PCD when using normal electrode of copper (Cu) and newly proposed graphitization catalyst electrode of copper nickel (CuNi). Two level full factorial design of experiment with 4 center points technique was used to study the influence of main and interaction effects of the machining parameter namely; pulse-on, pulse-off, sparking current, and electrode materials (categorical factor). The paper shows interesting discovery in which the newly proposed electrode presented positive impact to the machining performance. With the same machining parameters of finishing, CuNi delivered more than 100% better in Ra and MRR than ordinary Cu electrode.

MRR and TWR evaluation on electrical discharge machining of Ti-6Al-4V using tungsten : copper composite electrode

NASA Astrophysics Data System (ADS)

Prasanna, J.; Rajamanickam, S.; Amith Kumar, O.; Karthick Raj, G.; Sathya Narayanan, P. V. V.

2017-05-01

In this paper Ti-6Al-4V used as workpiece material and it is keenly seen in variety of field including medical, chemical, marine, automotive, aerospace, aviation, electronic industries, nuclear reactor, consumer products etc., The conventional machining of Ti-6Al-4V is very difficult due to its distinctive properties. The Electrical Discharge Machining (EDM) is right choice of machining this material. The tungsten copper composite material is employed as tool material. The gap voltage, peak current, pulse on time and duty factor is considered as the machining parameter to analyze the machining characteristics Material Removal Rate (MRR) and Tool Wear Rate (TWR). The Taguchi method is provided to work for finding the significant parameter of EDM. It is found that for MRR significant parameters rated in the following order Gap Voltage, Pulse On-Time, Peak Current and Duty Factor. On the other hand for TWR significant parameters are listed in line of Gap Voltage, Duty Factor, Peak Current and Pulse On-Time.

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

The Effects of Operational Parameters on a Mono-wire Cutting System: Efficiency in Marble Processing

NASA Astrophysics Data System (ADS)

Yilmazkaya, Emre; Ozcelik, Yilmaz

2016-02-01

Mono-wire block cutting machines that cut with a diamond wire can be used for squaring natural stone blocks and the slab-cutting process. The efficient use of these machines reduces operating costs by ensuring less diamond wire wear and longer wire life at high speeds. The high investment costs of these machines will lead to their efficient use and reduce production costs by increasing plant efficiency. Therefore, there is a need to investigate the cutting performance parameters of mono-wire cutting machines in terms of rock properties and operating parameters. This study aims to investigate the effects of the wire rotational speed (peripheral speed) and wire descending speed (cutting speed), which are the operating parameters of a mono-wire cutting machine, on unit wear and unit energy, which are the performance parameters in mono-wire cutting. By using the obtained results, cuttability charts for each natural stone were created on the basis of unit wear and unit energy values, cutting optimizations were performed, and the relationships between some physical and mechanical properties of rocks and the optimum cutting parameters obtained as a result of the optimization were investigated.

Properties of CF/PA6 friction spun hybrid yarns for textile reinforced thermoplastic composites

NASA Astrophysics Data System (ADS)

Hasan, MMB; Nitsche, S.; Abdkader, A.; Cherif, Ch

2017-10-01

Due to their excellent strength, rigidity and damping properties as well as low weight, carbon fibre reinforced composites (CFRC) are widely being used for load bearing structures. On the other hand, with an increased demand und usage of CFRCs, effective methods to re-use waste carbon fibre (CF) materials, which are recoverable either from the process scraps or from the end-of-life components are attracting increased attention. In this paper, hybrid yarns consisting of staple CF and polyamide 6 (PA 6) are manufactured on a DREF-3000 friction spinning machine with various machine parameters such as spinning drum speed and suction air pressure. The relationship between different textile physical properties of the hybrid yarns, such as tensile strength and elongation with different spinning parameters and CF content of hybrid yarn is investigated. Furthermore, the tensile properties of uni-directional (UD) composites manufactured from the developed hybrid yarn shows 80% of the UD composite strength made from CF filament yarn.

Apparatus and method for fluid analysis

DOEpatents

Wilson, Bary W.; Peters, Timothy J.; Shepard, Chester L.; Reeves, James H.

2004-11-02

The present invention is an apparatus and method for analyzing a fluid used in a machine or in an industrial process line. The apparatus has at least one meter placed proximate the machine or process line and in contact with the machine or process fluid for measuring at least one parameter related to the fluid. The at least one parameter is a standard laboratory analysis parameter. The at least one meter includes but is not limited to viscometer, element meter, optical meter, particulate meter, and combinations thereof.

Determination of the technical constants of laminates in oblique directions

NASA Technical Reports Server (NTRS)

Vidouse, F.

1979-01-01

An off-axis tensile test theory based on Hooke's Law is applied to glass fiber reinforced laminates. A corrective parameter dependent on the characteristics of the strain gauge used is introduced by testing machines set up for isotropic materials. Theoretical results for a variety of strain gauges are compared with those obtained by a finite element method and with experimental results obtained on laminates reinforced with glass.

High Throughput Determination of Mercury in Tobacco and Mainstream Smoke from Little Cigars

PubMed Central

Fresquez, Mark R.; Gonzalez-Jimenez, Nathalie; Gray, Naudia; Watson, Clifford H.; Pappas, R. Steven

2015-01-01

A method was developed that utilizes a platinum trap for mercury from mainstream tobacco smoke which represents an improvement over traditional approaches that require impingers and long sample preparation procedures. In this approach, the trapped mercury is directly released for analysis by heating the trap in a direct mercury analyzer. The method was applied to the analysis of mercury in the mainstream smoke of little cigars. The mercury levels in little cigar smoke obtained under Health Canada Intense smoking machine conditions ranged from 7.1 × 10−3 mg/m3 to 1.2 × 10−2 mg/m3. These air mercury levels exceed the chronic inhalation Minimal Risk Level corrected for intermittent exposure to metallic mercury (e.g., 1 or 2 hours per day, 5 days per week) determined by the Agency for Toxic Substances and Disease Registry. Multivariate statistical analysis was used to assess associations between mercury levels and little cigar physical design properties. Filter ventilation was identified as the principal physical parameter influencing mercury concentrations in mainstream little cigar smoke generated under ISO machine smoking conditions. With filter ventilation blocked under Health Canada Intense smoking conditions, mercury concentrations in tobacco and puff number (smoke volume) were the primary physical parameters that influenced mainstream smoke mercury concentrations. PMID:26051388

“Investigations on the machinability of Waspaloy under dry environment”

NASA Astrophysics Data System (ADS)

Deepu, J.; Kuppan, P.; SBalan, A. S.; Oyyaravelu, R.

2016-09-01

Nickel based superalloy, Waspaloy is extensively used in gas turbine, aerospace and automobile industries because of their unique combination of properties like high strength at elevated temperatures, resistance to chemical degradation and excellent wear resistance in many hostile environments. It is considered as one of the difficult to machine superalloy due to excessive tool wear and poor surface finish. The present paper is an attempt for removing cutting fluids from turning process of Waspaloy and to make the processes environmentally safe. For this purpose, the effect of machining parameters such as cutting speed and feed rate on the cutting force, cutting temperature, surface finish and tool wear were investigated barrier. Consequently, the strength and tool wear resistance and tool life increased significantly. Response Surface Methodology (RSM) has been used for developing and analyzing a mathematical model which describes the relationship between machining parameters and output variables. Subsequently ANOVA was used to check the adequacy of the regression model as well as each machining variables. The optimal cutting parameters were determined based on multi-response optimizations by composite desirability approach in order to minimize cutting force, average surface roughness and maximum flank wear. The results obtained from the experiments shown that machining of Waspaloy using coated carbide tool with special ranges of parameters, cutting fluid could be completely removed from machining process

Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

NASA Astrophysics Data System (ADS)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.

Measurement and Analysis of Drapeability Effects of Warp-Knit NCF with a Standardised, Automated Testing Device

NASA Astrophysics Data System (ADS)

Christ, Mirko; Miene, Andrea; Mörschel, Ulrich

2017-08-01

Characterising the drapeability of reinforcement fabrics, is one of the most sought after abilities of those designing composite processes and components. This is not surprising as composite processes are being considered in a greater range of fields and applications. Drapeability effects are formed by the irregular rearrangement of fibres. This displacement can occur within the textile plane and result in fibre disorientations, undulations and gaps or the fibres can be pushed into the third dimension - forming wrinkles or loops. To measure such effects in non-crimp fabrics, the Textechno Drapetest automatic drapeability tester was developed. To show its viability as a tool for composite engineering, a set of fabrics was chosen to show that the influence of textile design parameters on drapeability effects is now quantifiable. The Textechno Drapetest uses a sophisticated digital image analysis system to measure the position and direction of fibres and conclude from this information on the extent and intensity of drapeability effects in the textile surface. To measure effects outside the surface, i.e. wrinkles, a laser triangulation sensor is employed. The textiles were varied in the production parameters of stitch point distance in machine direction (MD) and cross direction (CD), the weight per area, and the stitch pattern (tricot and chain). The measurements showed that the new test method is capable of measuring the effects that were expected from classical test setups as well as a range of additional effects. From the results a significant influence of the stitch yarn on the formation of effects can be deduced. Especially the density of stitch points is a parameter that lets the textile producer control the behaviour of the textile when they are formed into a doubly curved three dimensional shape. To control the gap formation, however, the spacing of the stitch points in machine or in crosswise direction is also of importance with a shorter stitch length decreasing the forming of gaps more than a tighter stitch yarn pitch.

Influence of Wire Electrical Discharge Machining (WEDM) process parameters on surface roughness

NASA Astrophysics Data System (ADS)

Yeakub Ali, Mohammad; Banu, Asfana; Abu Bakar, Mazilah

2018-01-01

In obtaining the best quality of engineering components, the quality of machined parts surface plays an important role. It improves the fatigue strength, wear resistance, and corrosion of workpiece. This paper investigates the effects of wire electrical discharge machining (WEDM) process parameters on surface roughness of stainless steel using distilled water as dielectric fluid and brass wire as tool electrode. The parameters selected are voltage open, wire speed, wire tension, voltage gap, and off time. Empirical model was developed for the estimation of surface roughness. The analysis revealed that off time has a major influence on surface roughness. The optimum machining parameters for minimum surface roughness were found to be at a 10 V open voltage, 2.84 μs off time, 12 m/min wire speed, 6.3 N wire tension, and 54.91 V voltage gap.

On Docking, Scoring and Assessing Protein-DNA Complexes in a Rigid-Body Framework

PubMed Central

Parisien, Marc; Freed, Karl F.; Sosnick, Tobin R.

2012-01-01

We consider the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. Additional tests with large decoy sets probe the efficacy of two published statistically derived scoring functions that contain a huge number of parameters. In contrast, we demonstrate that state-of-the-art machine learning techniques can enormously reduce the number of parameters required, thereby identifying the relevant docking features using a miniscule fraction of the number of parameters in the prior works. The present machine learning study considers a 300 dimensional vector (dependent on only 15 parameters), termed the Chemical Context Profile (CCP), where each dimension reflects a specific type of protein amino acid-nucleic acid base interaction. The CCP is designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. Our objective function is the Chemical Context Discrepancy (CCD), which is defined as the angle between the native system's CCP vector and the decoy's vector and which serves as a substitute for the more commonly used root mean squared deviation (RMSD). We demonstrate that the CCP provides a useful scoring function when certain dimensions are properly weighted. Finally, we explore how the amino acids on a protein's surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA. PMID:22393431

Application of Support Vector Machine to Forex Monitoring

NASA Astrophysics Data System (ADS)

Kamruzzaman, Joarder; Sarker, Ruhul A.

Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

Analysis of Flatness Deviations for Austenitic Stainless Steel Workpieces after Efficient Surface Machining

NASA Astrophysics Data System (ADS)

Nadolny, K.; Kapłonek, W.

2014-08-01

The following work is an analysis of flatness deviations of a workpiece made of X2CrNiMo17-12-2 austenitic stainless steel. The workpiece surface was shaped using efficient machining techniques (milling, grinding, and smoothing). After the machining was completed, all surfaces underwent stylus measurements in order to obtain surface flatness and roughness parameters. For this purpose the stylus profilometer Hommel-Tester T8000 by Hommelwerke with HommelMap software was used. The research results are presented in the form of 2D surface maps, 3D surface topographies with extracted single profiles, Abbott-Firestone curves, and graphical studies of the Sk parameters. The results of these experimental tests proved the possibility of a correlation between flatness and roughness parameters, as well as enabled an analysis of changes in these parameters from shaping and rough grinding to finished machining. The main novelty of this paper is comprehensive analysis of measurement results obtained during a three-step machining process of austenitic stainless steel. Simultaneous analysis of individual machining steps (milling, grinding, and smoothing) enabled a complementary assessment of the process of shaping the workpiece surface macro- and micro-geometry, giving special consideration to minimize the flatness deviations

Good Models Gone Bad: Quantifying and Predicting Parameter-Induced Climate Model Simulation Failures

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Klein, R.; Tannahill, J.; Brandon, S.; Covey, C. C.; Domyancic, D.; Ivanova, D. P.

2012-12-01

Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Statistical analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation failures of the Parallel Ocean Program (POP2). About 8.5% of our POP2 runs failed for numerical reasons at certain combinations of parameter values. We apply support vector machine (SVM) classification from the fields of pattern recognition and machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. The SVM classifiers readily predict POP2 failures in an independent validation ensemble, and are subsequently used to determine the causes of the failures via a global sensitivity analysis. Four parameters related to ocean mixing and viscosity are identified as the major sources of POP2 failures. Our method can be used to improve the robustness of complex scientific models to parameter perturbations and to better steer UQ ensembles. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).

New classification methods on singularity of mechanism

NASA Astrophysics Data System (ADS)

Luo, Jianguo; Han, Jianyou

2010-07-01

Based on the analysis of base and methods of singularity of mechanism, four methods obtained according to the factors of moving states of mechanism and cause of singularity and property of linear complex of singularity and methods in studying singularity, these bases and methods can't reflect the direct property and systematic property and controllable property of the structure of mechanism in macro, thus can't play an excellent role in guiding to evade the configuration before the appearance of singularity. In view of the shortcomings of forementioned four bases and methods, six new methods combined with the structure and exterior phenomena and motion control of mechanism directly and closely, classfication carried out based on the factors of moving base and joint component and executor and branch and acutating source and input parameters, these factors display the systemic property in macro, excellent guiding performance can be expected in singularity evasion and machine design and machine control based on these new bases and methods.

Research on the effect of coverage rate on the surface quality in laser direct writing process

NASA Astrophysics Data System (ADS)

Pan, Xuetao; Tu, Dawei

2017-07-01

Direct writing technique is usually used in femtosecond laser two-photon micromachining. The size of the scanning step is an important factor affecting the surface quality and machining efficiency of micro devices. According to the mechanism of two-photon polymerization, combining the distribution function of light intensity and the free radical concentration theory, we establish the mathematical model of coverage of solidification unit, then analyze the effect of coverage on the machining quality and efficiency. Using the principle of exposure equivalence, we also obtained the analytic expressions of the relationship among the surface quality characteristic parameters of microdevices and the scanning step, and carried out the numerical simulation and experiment. The results show that the scanning step has little influence on the surface quality of the line when it is much smaller than the size of the solidification unit. However, with increasing scanning step, the smoothness of line surface is reduced rapidly, and the surface quality becomes much worse.

Tool life and surface integrity aspects when drilling nickel alloy

NASA Astrophysics Data System (ADS)

Kannan, S.; Pervaiz, S.; Vincent, S.; Karthikeyan, R.

2018-04-01

Nickel based super alloys manufactured through powder metallurgy (PM) route are required to increase the operational efficiency of gas turbine engines. They are material of choice for high pressure components due to their superior high temperature strength, excellent corrosion, oxidation and creep resistance. This unique combination of mechanical and thermal properties makes them even more difficult-to-machine. In this paper, the hole making process using coated carbide inserts by drilling and plunge milling for a nickel-based powder metallurgy super alloy has been investigated. Tool life and process capability studies were conducted using optimized process parameters using high pressure coolants. The experimental trials were directed towards an assessment of the tendency for surface malformations and detrimental residual stress profiles. Residual stresses in both the radial and circumferential directions have been evaluated as a function of depth from the machined surface using the target strain gauge / center hole drilling method. Circumferential stresses near workpiece surface and at depth of 512 µm in the starting material was primarily circumferential compression which was measured to be average of –404 MPa. However, the radial stresses near workpiece surface was tensile and transformed to be compressive in nature at depth of 512 µm in the starting material (average: -87 Mpa). The magnitude and the depth below the machined surface in both radial and circumferential directions were primarily tensile in nature which increased with hole number due to a rise of temperature at the tool–workpiece interface with increasing tool wear. These profiles are of critical importance for the selection of cutting strategies to ensure avoidance/minimization of tensile residual stresses that can be detrimental to the fatigue performance of the components. These results clearly show a tendency for the circumferential stresses to be more tensile than the radial stresses. Overall the results indicate that the effect of drilling and milling parameters is most marked in terms of surface quality in the circumferential direction. Material removal rates and tool flank wear must be maintained within the control limits to maintain hole integrity.

Research of Surface Roughness Anisotropy

NASA Astrophysics Data System (ADS)

Bulaha, N.; Rudzitis, J.; Lungevics, J.; Linins, O.; Krizbergs, J.

2017-04-01

The authors of the paper have investigated surfaces with irregular roughness for the purpose of determination of roughness spacing parameters perpendicularly to machining traces - RSm1 and parallel to them - RSm2, as well as checking the relationship between the surface anisotropy coefficient c and surface aspect ratio Str from the standard LVS EN ISO 25178-2. Surface roughness measurement experiments with 11 surfaces show that measuring equipment values of mean spacing of profile irregularities in the longitudinal direction are not reliable due to the divergence of surface mean plane and roughness profile mean line. After the additional calculations it was stated that parameter Str can be used for determination of parameter RSm2 and roughness anisotropy evaluation for grinded, polished, friction surfaces and other surfaces with similar characteristics.

Effect of Machining Parameters on Oxidation Behavior of Mild Steel

NASA Astrophysics Data System (ADS)

Majumdar, P.; Shekhar, S.; Mondal, K.

2015-01-01

This study aims to find out a correlation between machining parameters, resultant microstructure, and isothermal oxidation behavior of lathe-machined mild steel in the temperature range of 660-710 °C. The tool rake angles "α" used were +20°, 0°, and -20°, and cutting speeds used were 41, 232, and 541 mm/s. Under isothermal conditions, non-machined and machined mild steel samples follow parabolic oxidation kinetics with activation energy of 181 and ~400 kJ/mol, respectively. Exaggerated grain growth of the machined surface was observed, whereas, the center part of the machined sample showed minimal grain growth during oxidation at higher temperatures. Grain growth on the surface was attributed to the reduction of strain energy at high temperature oxidation, which was accumulated on the sub-region of the machined surface during machining. It was also observed that characteristic surface oxide controlled the oxidation behavior of the machined samples. This study clearly demonstrates the effect of equivalent strain, roughness, and grain size due to machining, and subsequent grain growth on the oxidation behavior of the mild steel.

Generative Modeling for Machine Learning on the D-Wave

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thulasidasan, Sunil

These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.

A method to identify the main mode of machine tool under operating conditions

NASA Astrophysics Data System (ADS)

Wang, Daming; Pan, Yabing

2017-04-01

The identification of the modal parameters under experimental conditions is the most common procedure when solving the problem of machine tool structure vibration. However, the influence of each mode on the machine tool vibration in real working conditions remains unknown. In fact, the contributions each mode makes to the machine tool vibration during machining process are different. In this article, an active excitation modal analysis is applied to identify the modal parameters in operational condition, and the Operating Deflection Shapes (ODS) in frequencies of high level vibration that affect the quality of machining in real working conditions are obtained. Then, the ODS is decomposed by the mode shapes which are identified in operational conditions. So, the contributions each mode makes to machine tool vibration during machining process are got by decomposition coefficients. From the previous steps, we can find out the main modes which effect the machine tool more significantly in working conditions. This method was also verified to be effective by experiments.

Single molecule thermodynamics in biological motors.

PubMed

Taniguchi, Yuichi; Karagiannis, Peter; Nishiyama, Masayoshi; Ishii, Yoshiharu; Yanagida, Toshio

2007-04-01

Biological molecular machines use thermal activation energy to carry out various functions. The process of thermal activation has the stochastic nature of output events that can be described according to the laws of thermodynamics. Recently developed single molecule detection techniques have allowed each distinct enzymatic event of single biological machines to be characterized providing clues to the underlying thermodynamics. In this study, the thermodynamic properties in the stepping movement of a biological molecular motor have been examined. A single molecule detection technique was used to measure the stepping movements at various loads and temperatures and a range of thermodynamic parameters associated with the production of each forward and backward step including free energy, enthalpy, entropy and characteristic distance were obtained. The results show that an asymmetry in entropy is a primary factor that controls the direction in which the motor will step. The investigation on single molecule thermodynamics has the potential to reveal dynamic properties underlying the mechanisms of how biological molecular machines work.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Research on On-Line Modeling of Fed-Batch Fermentation Process Based on v-SVR

NASA Astrophysics Data System (ADS)

Ma, Yongjun

The fermentation process is very complex and non-linear, many parameters are not easy to measure directly on line, soft sensor modeling is a good solution. This paper introduces v-support vector regression (v-SVR) for soft sensor modeling of fed-batch fermentation process. v-SVR is a novel type of learning machine. It can control the accuracy of fitness and prediction error by adjusting the parameter v. An on-line training algorithm is discussed in detail to reduce the training complexity of v-SVR. The experimental results show that v-SVR has low error rate and better generalization with appropriate v.

Crack curving in a ductile pressurized fuselage

NASA Astrophysics Data System (ADS)

Lam, Paul W.

Moire interferometry was used to study crack tip displacement fields of a biaxially loaded cruciform type 0.8mm thick 2024-T3 aluminum specimen with various tearstrap reinforcement configurations: Unreinforced, Bonded, Bonded+Riveted, and Machined Pad-up. A program was developed using the commercially available code Matlab to derive strain, stress, and integral parameters from the experimental displacements. An FEM model of the crack tip area, with experimental displacements as boundary conditions, was used to validate FEM calculations of crack tip parameters. The results indicate that T*-integral parameter reaches a value of approximately 120 MPa-m0.5 during stable crack propagation which agrees with previously published values for straight cracks in the same material. The approximate computation method employed in this study uses a partial contour around the crack tip that neglects the contribution from the portion behind the crack tip where there is significant unloading. Strain distributions around the crack tip were obtained from experimental displacements and indicate that Maximum Principal Strain or Equivalent Strain can predict the direction of crack propagation, and is generally comparable with predictions using the Erdogan-Sih and Kosai-Ramulu-Kobayashi criteria. The biaxial tests to failure showed that the Machined Pad-up specimen carried the highest load, with the Bonded specimen next, at 78% of the Machined Pad-up value. The Bonded+Riveted specimen carried a lower load than the Bonded, at 67% of the Machined Pad-up value, which was the same as that carried by the Unreinforced specimen. The tearstraps of the bonded specimens remained intact after the specimen failed while the integrally machined reinforcement broke with the specimen. FEM studies were also made of skin flapping in typical Narrow and Wide-body fuselage sections, both containing the same crack path from a full-scale fatigue test of a Narrow-body fuselage. Results indicate that the magnitude of CTOA and CTOD depends on the structural geometry, and including plasticity increases the crack tip displacements. An estimate of the strain in the skin flaps at the crack tip may indicate the tendency for flapping. Out-of-plane effects become significant as the crack propagates and curves.

Aspects of the dimensional changes of jersey structures after knitting process

NASA Astrophysics Data System (ADS)

Szabo, M.; Barbu, I.; Jiaru, L.

2017-08-01

The study proposes a statistical analysis by applying a mathematical model for the study of the dimensional changes of jersey structures made of 100% cotton yarn, with 58/1 metric count of yarn. The Structures are presented as tubular knitted metrage and are designed for underwear and/or outer garments. By analysing the jersey structures, from dimensional stability point of view, there can be observed that values in the limits are within the ±2% interval, values which are considered appropriate. Following the experimental researches, there are proposed solutions for the reduction of dimensional changes on both directions of the knit, on the stich course direction and also on the stich courses in vertical direction, being analyzed the behaviour of the knitted fabrics during relaxation after knitting process. The problem of the dimensional stability of the knitted fabrics is extensive researched. The knitted structures are elastic structures, this being a reason for which dimensional stability will always be a topical theme. The jersey structures, due to the distribution of the platinum loop in the knit plane, due to the relative small number of yarn-yarn contact points that causes the threads to slide into the structure, due to the spiral of the tubular metrage structure, are among those whose dimensional stability is difficult to control. The technical characteristics of the yarns, the technical characteristics of the knitting machines and the technological parameters of the knitting machine are the elements which will be correlated in order to obtain structures with minimum dimensional changes. In order to obtain knitted structures with adequate dimensional stability, this means within ±2%, it is necessary that the dimensional changes during the relaxation periods after knitting and chemical finishing being minimum. For this, all the processes to be applied will be conducted with appropriate and uniform tensions throughout the technological flow. The relaxation periods of 72 hours should be strictly respected, folded and under standard atmospheric conditions, both after knitting and after chemical finishing. The jersey structures are plane structured made on knitting machines equiped with font. There will be analyzed the dimensional changes of the jersey structures made of 100% cotton yarn, Nm 58/1, after the relaxation after knitting process througout the corelation between the technical characteristics of the yarns, of the technological parameter of the knitting operation and of some technical characteristici of the knitting machine.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Automatic classification of protein structures using physicochemical parameters.

PubMed

Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

2014-09-01

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Influence of Cutting Parameters and Tool Wear on the Surface Integrity of Cobalt-Based Stellite 6 Alloy When Machined Under a Dry Cutting Environment

NASA Astrophysics Data System (ADS)

Yingfei, Ge; de Escalona, Patricia Muñoz; Galloway, Alexander

2017-01-01

The efficiency of a machining process can be measured by evaluating the quality of the machined surface and the tool wear rate. The research reported herein is mainly focused on the effect of cutting parameters and tool wear on the machined surface defects, surface roughness, deformation layer and residual stresses when dry milling Stellite 6, deposited by overlay on a carbon steel surface. The results showed that under the selected cutting conditions, abrasion, diffusion, peeling, chipping and breakage were the main tool wear mechanisms presented. Also the feed rate was the primary factor affecting the tool wear with an influence of 83%. With regard to the influence of cutting parameters on the surface roughness, the primary factors were feed rate and cutting speed with 57 and 38%, respectively. In addition, in general, as tool wear increased, the surface roughness increased and the deformation layer was found to be influenced more by the cutting parameters rather than the tool wear. Compressive residual stresses were observed in the un-machined surface, and when machining longer than 5 min, residual stress changed 100% from compression to tension. Finally, results showed that micro-crack initiation was the main mechanism for chip formation.

Effect of cutting parameters on surface finish and machinability of graphite reinforced Al-8011 matrix composite

NASA Astrophysics Data System (ADS)

Anil, K. C.; Vikas, M. G.; Shanmukha Teja, B.; Sreenivas Rao, K. V.

2017-04-01

Many materials such as alloys, composites find their applications on the basis of machinability, cost and availability. In the present work, graphite (Grp) reinforced Aluminium 8011 is synthesized by convention stir casting process and Surface finish & machinability of prepared composite is examined by using lathe tool dynamometer attached with BANKA Lathe by varying the machining parameters like spindle speed, Depth of cut and Feed rate in 3 levels. Also, Roughness Average (Ra) of machined surfaces is measured by using Surface Roughness Tester (Mitutoyo SJ201). From the studies it is cleared that mechanical properties of a composites increases with addition of Grp and The cutting force were decreased with the reinforcement percentage and thus increases the machinability of composites and also results in increased surface finish.

Analyzing the effect of cutting parameters on surface roughness and tool wear when machining nickel based hastelloy - 276

NASA Astrophysics Data System (ADS)

Khidhir, Basim A.; Mohamed, Bashir

2011-02-01

Machining parameters has an important factor on tool wear and surface finish, for that the manufacturers need to obtain optimal operating parameters with a minimum set of experiments as well as minimizing the simulations in order to reduce machining set up costs. The cutting speed is one of the most important cutting parameter to evaluate, it clearly most influences on one hand, tool life, tool stability, and cutting process quality, and on the other hand controls production flow. Due to more demanding manufacturing systems, the requirements for reliable technological information have increased. For a reliable analysis in cutting, the cutting zone (tip insert-workpiece-chip system) as the mechanics of cutting in this area are very complicated, the chip is formed in the shear plane (entrance the shear zone) and is shape in the sliding plane. The temperature contributed in the primary shear, chamfer and sticking, sliding zones are expressed as a function of unknown shear angle on the rake face and temperature modified flow stress in each zone. The experiments were carried out on a CNC lathe and surface finish and tool tip wear are measured in process. Machining experiments are conducted. Reasonable agreement is observed under turning with high depth of cut. Results of this research help to guide the design of new cutting tool materials and the studies on evaluation of machining parameters to further advance the productivity of nickel based alloy Hastelloy - 276 machining.

A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

NASA Astrophysics Data System (ADS)

Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.

2015-08-01

Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT.

PubMed

Bizios, Dimitrios; Heijl, Anders; Hougaard, Jesper Leth; Bengtsson, Boel

2010-02-01

To compare the performance of two machine learning classifiers (MLCs), artificial neural networks (ANNs) and support vector machines (SVMs), with input based on retinal nerve fibre layer thickness (RNFLT) measurements by optical coherence tomography (OCT), on the diagnosis of glaucoma, and to assess the effects of different input parameters. We analysed Stratus OCT data from 90 healthy persons and 62 glaucoma patients. Performance of MLCs was compared using conventional OCT RNFLT parameters plus novel parameters such as minimum RNFLT values, 10th and 90th percentiles of measured RNFLT, and transformations of A-scan measurements. For each input parameter and MLC, the area under the receiver operating characteristic curve (AROC) was calculated. There were no statistically significant differences between ANNs and SVMs. The best AROCs for both ANN (0.982, 95%CI: 0.966-0.999) and SVM (0.989, 95% CI: 0.979-1.0) were based on input of transformed A-scan measurements. Our SVM trained on this input performed better than ANNs or SVMs trained on any of the single RNFLT parameters (p < or = 0.038). The performance of ANNs and SVMs trained on minimum thickness values and the 10th and 90th percentiles were at least as good as ANNs and SVMs with input based on the conventional RNFLT parameters. No differences between ANN and SVM were observed in this study. Both MLCs performed very well, with similar diagnostic performance. Input parameters have a larger impact on diagnostic performance than the type of machine classifier. Our results suggest that parameters based on transformed A-scan thickness measurements of the RNFL processed by machine classifiers can improve OCT-based glaucoma diagnosis.

Machinability of IPS Empress 2 framework ceramic.

PubMed

Schmidt, C; Weigl, P

2000-01-01

Using ceramic materials for an automatic production of ceramic dentures by CAD/CAM is a challenge, because many technological, medical, and optical demands must be considered. The IPS Empress 2 framework ceramic meets most of them. This study shows the possibilities for machining this ceramic with economical parameters. The long life-time requirement for ceramic dentures requires a ductile machined surface to avoid the well-known subsurface damages of brittle materials caused by machining. Slow and rapid damage propagation begins at break outs and cracks, and limits life-time significantly. Therefore, ductile machined surfaces are an important demand for machine dental ceramics. The machining tests were performed with various parameters such as tool grain size and feed speed. Denture ceramics were machined by jig grinding on a 5-axis CNC milling machine (Maho HGF 500) with a high-speed spindle up to 120,000 rpm. The results of the wear test indicate low tool wear. With one tool, you can machine eight occlusal surfaces including roughing and finishing. One occlusal surface takes about 60 min machining time. Recommended parameters for roughing are middle diamond grain size (D107), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 1000 mm/min, depth of cut a(e) = 0.06 mm, width of contact a(p) = 0.8 mm, and for finishing ultra fine diamond grain size (D46), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 100 mm/min, depth of cut a(e) = 0.02 mm, width of contact a(p) = 0.8 mm. The results of the machining tests give a reference for using IPS Empress(R) 2 framework ceramic in CAD/CAM systems. Copyright 2000 John Wiley & Sons, Inc.

Condition monitoring of turning process using infrared thermography technique - An experimental approach

NASA Astrophysics Data System (ADS)

Prasad, Balla Srinivasa; Prabha, K. Aruna; Kumar, P. V. S. Ganesh

2017-03-01

In metal cutting machining, major factors that affect the cutting tool life are machine tool vibrations, tool tip/chip temperature and surface roughness along with machining parameters like cutting speed, feed rate, depth of cut, tool geometry, etc., so it becomes important for the manufacturing industry to find the suitable levels of process parameters for obtaining maintaining tool life. Heat generation in cutting was always a main topic to be studied in machining. Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for development of the effective monitoring of tool condition monitoring systems with improved accuracy. From a process improvement point of view, it is definitely more advantageous to proactively monitor quality directly in the process instead of the product, so that the consequences of a defective part can be minimized or even eliminated. In the present work, a real time process monitoring method is explored using multiple sensors. It focuses on the development of a test bed for monitoring the tool condition in turning of AISI 316L steel by using both coated and uncoated carbide inserts. Proposed tool condition monitoring (TCM) is evaluated in the high speed turning using multiple sensors such as Laser Doppler vibrometer and infrared thermography technique. The results indicate the feasibility of using the dominant frequency of the vibration signals for the monitoring of high speed turning operations along with temperatures gradient. A possible correlation is identified in both regular and irregular cutting tool wear. While cutting speed and feed rate proved to be influential parameter on the depicted temperatures and depth of cut to be less influential. Generally, it is observed that lower heat and temperatures are generated when coated inserts are employed. It is found that cutting temperatures are gradually increased as edge wear and deformation developed.

Modeling and Analysis of CNC Milling Process Parameters on Al3030 based Composite

NASA Astrophysics Data System (ADS)

Gupta, Anand; Soni, P. K.; Krishna, C. M.

2018-04-01

The machining of Al3030 based composites on Computer Numerical Control (CNC) high speed milling machine have assumed importance because of their wide application in aerospace industries, marine industries and automotive industries etc. Industries mainly focus on surface irregularities; material removal rate (MRR) and tool wear rate (TWR) which usually depends on input process parameters namely cutting speed, feed in mm/min, depth of cut and step over ratio. Many researchers have carried out researches in this area but very few have taken step over ratio or radial depth of cut also as one of the input variables. In this research work, the study of characteristics of Al3030 is carried out at high speed CNC milling machine over the speed range of 3000 to 5000 r.p.m. Step over ratio, depth of cut and feed rate are other input variables taken into consideration in this research work. A total nine experiments are conducted according to Taguchi L9 orthogonal array. The machining is carried out on high speed CNC milling machine using flat end mill of diameter 10mm. Flatness, MRR and TWR are taken as output parameters. Flatness has been measured using portable Coordinate Measuring Machine (CMM). Linear regression models have been developed using Minitab 18 software and result are validated by conducting selected additional set of experiments. Selection of input process parameters in order to get best machining outputs is the key contributions of this research work.

Method and apparatus for monitoring machine performance

DOEpatents

Smith, Stephen F.; Castleberry, Kimberly N.

1996-01-01

Machine operating conditions can be monitored by analyzing, in either the time or frequency domain, the spectral components of the motor current. Changes in the electric background noise, induced by mechanical variations in the machine, are correlated to changes in the operating parameters of the machine.

Standard guidelines of care: laser and IPL hair reduction.

PubMed

Buddhadev, Rajesh M

2008-01-01

Laser-assisted hair removal, Laser hair removal, Laser and light-assisted hair removal, Laser and light-assisted, long-term hair reduction, IPL photodepilation, LHE photodepilation; all these are acceptable synonyms. Laser (Ruby, Nd Yag, Alexandrite, Diode), intense pulse light, light and heat energy system are the different light-/Laser-based systems used for hair removal; each have its advantages and disadvantages. The word "LONG-TERM HAIR REDUCTION" should be used rather than permanent hair removal. Patient counseling is essential about the need for multiple sessions. PHYSICIANS' QUALIFICATIONS: Laser hair removal may be practiced by any dermatologist, who has received adequate background training during postgraduation or later at a centre that provides education and training in Lasers or in focused workshops providing such training. The dermatologist should have adequate knowledge of the machines, the parameters and aftercare. The physician may allow the actual procedure to be performed under his/her direct supervision by a trained nurse assistant/junior doctor. However, the final responsibility for the procedure would lie with the physician. The procedure may be performed in the physician's minor procedure room. Investigations to rule out any underlying cause for hair growth are important; concurrent drug therapy may be needed. Laser parameters vary with area, type of hair, and the machine used. Full knowledge about the machine and cooling system is important. Future maintenance treatments may be needed.

Identification of Technological Parameters of Ni-Alloys When Machining by Monolithic Ceramic Milling Tool

NASA Astrophysics Data System (ADS)

Czán, Andrej; Kubala, Ondrej; Danis, Igor; Czánová, Tatiana; Holubják, Jozef; Mikloš, Matej

2017-12-01

The ever-increasing production and the usage of hard-to-machine progressive materials are the main cause of continual finding of new ways and methods of machining. One of these ways is the ceramic milling tool, which combines the pros of conventional ceramic cutting materials and pros of conventional coating steel-based insert. These properties allow to improve cutting conditions and so increase the productivity with preserved quality known from conventional tools usage. In this paper, there is made the identification of properties and possibilities of this tool when machining of hard-to-machine materials such as nickel alloys using in airplanes engines. This article is focused on the analysis and evaluation ordinary technological parameters and surface quality, mainly roughness of surface and quality of machined surface and tool wearing.

Improving the performance of extreme learning machine for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Li, Jiaojiao; Du, Qian; Li, Wei; Li, Yunsong

2015-05-01

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

Nondimensional parameter for conformal grinding: combining machine and process parameters

NASA Astrophysics Data System (ADS)

Funkenbusch, Paul D.; Takahashi, Toshio; Gracewski, Sheryl M.; Ruckman, Jeffrey L.

1999-11-01

Conformal grinding of optical materials with CNC (Computer Numerical Control) machining equipment can be used to achieve precise control over complex part configurations. However complications can arise due to the need to fabricate complex geometrical shapes at reasonable production rates. For example high machine stiffness is essential, but the need to grind 'inside' small or highly concave surfaces may require use of tooling with less than ideal stiffness characteristics. If grinding generates loads sufficient for significant tool deflection, the programmed removal depth will not be achieved. Moreover since grinding load is a function of the volumetric removal rate the amount of load deflection can vary with location on the part, potentially producing complex figure errors. In addition to machine/tool stiffness and removal rate, load generation is a function of the process parameters. For example by reducing the feed rate of the tool into the part, both the load and resultant deflection/removal error can be decreased. However this must be balanced against the need for part through put. In this paper a simple model which permits combination of machine stiffness and process parameters into a single non-dimensional parameter is adapted for a conformal grinding geometry. Errors in removal can be minimized by maintaining this parameter above a critical value. Moreover, since the value of this parameter depends on the local part geometry, it can be used to optimize process settings during grinding. For example it may be used to guide adjustment of the feed rate as a function of location on the part to eliminate figure errors while minimizing the total grinding time required.

Influence of cutting data on surface quality when machining 17-4 PH stainless steel

NASA Astrophysics Data System (ADS)

Popovici, T. D.; Dijmărescu, M. R.

2017-08-01

The aim of the research presented in this paper is to analyse the cutting data influence upon surface quality for 17-4 PH stainless steel milling machining. The cutting regime parameters considered for the experiments were established using cutting regimes from experimental researches or from industrial conditions as basis, within the recommended ranges. The experimental program structure was determined by taking into account compatibility and orthogonality conditions, minimal use of material and labour. The machined surface roughness was determined by measuring the Ra roughness parameter, followed by surface profile registration in the form of graphics which were saved on a computer with MarSurf PS1Explorer software. Based on Ra roughness parameter, maximum values were extracted from these graphics and the influence charts of the cutting regime parameters upon surface roughness were traced using Microsoft Excel software. After a thorough analysis of the resulting data, relevant conclusions were drawn, presenting the interdependence between the surface roughness of the machined 17-4 PH samples and the cutting data variation.

Machining of Molybdenum by EDM-EP and EDC Processes

NASA Astrophysics Data System (ADS)

Wu, K. L.; Chen, H. J.; Lee, H. M.; Lo, J. S.

2017-12-01

Molybdenum metal (Mo) can be machined with conventional tools and equipment, however, its refractory propertytends to chip when being machined. In this study, the nonconventional processes of electrical discharge machining (EDM) and electro-polishing (EP) have been conducted to investigate the machining of Mo metal and fabrication of Mo grid. Satisfactory surface quality was obtained using appropriate EDM parameters of Ip ≦ 3A and Ton < 80μs at a constant pulse interval of 100μs. The finished Mometal has accomplished by selecting appropriate EP parameters such as electrolyte flow rate of 0.42m/s under EP voltage of 50V and flush time of 20 sec to remove the recast layer and craters on the surface of Mo metal. The surface roughness of machined Mo metal can be improved from Ra of 0.93μm (Rmax = 8.51μm) to 0.23μm (Rmax = 1.48μm). Machined Mo metal surface, when used as grid component in electron gun, needs to be modified by coating materials with high work function, such as silicon carbide (SiC). The main purpose of this study is to explore the electrical discharge coating (EDC) process for coating the SiC layer on EDMed Mo metal. Experimental results proved that the appropriate parameters of Ip = 5A and Ton = 50μs at Toff = 10μs can obtain the deposit with about 60μm thickness. The major phase of deposit on machined Mo surface was SiC ceramic, while the minor phases included MoSi2 and/or SiO2 with the presence of free Si due to improper discharging parameters and the use of silicone oil as the dielectric fluid.

Contributions a la caracterisation et a l'amelioration de l'usinabilite de pieces d'acier elaborees par metallurgie des poudres

NASA Astrophysics Data System (ADS)

Boilard, Patrick

Even though powder metallurgy (P/M) is a near net shape process, a large number of parts still require one or more machining operations during the course of their elaboration and/or their finishing. The main objectives of the work presented in this thesis are centered on the elaboration of blends with enhanced machinability, as well as helping with the definition and in the characterization of the machinability of P/M parts. Enhancing machinability can be done in various ways, through the use of machinability additives and by decreasing the amount of porosity of the parts. These different ways of enhancing machinability have been investigated thoroughly, by systematically planning and preparing series of samples in order to obtain valid and repeatable results leading to meaningful conclusions relevant to the P/M domain. Results obtained during the course of the work are divided into three main chapters: (1) the effect of machining parameters on machinability, (2) the effect of additives on machinability, and (3) the development and the characterization of high density parts obtained by liquid phase sintering. Regarding the effect of machining parameters on machinability, studies were performed on parameters such as rotating speed, feed, tool position and diameter of the tool. Optimal cutting parameters are found for drilling operations performed on a standard FC-0208 blend, for different machinability criteria. Moreover, study of material removal rates shows the sensitivity of the machinability criteria for different machining parameters and indicates that thrust force is more regular than tool wear and slope of the drillability curve in the characterization of machinability. The chapter discussing the effect of various additives on machinability reveals many interesting results. First, work carried out on MoS2 additions reveals the dissociation of this additive and the creation of metallic sulphides (namely CuxS sulphides) when copper is present. Results also show that it is possible to reduce the amount of MoS2 in the blend so as to lower the dimensional change and the cost (blend Mo8A), while enhancing machinability and keeping hardness values within the same range (70 HRB). Second, adding enstatite (MgO·SiO2) permits the observation of the mechanisms occurring with the use of this additive. It is found that the stability of enstatite limits the diffusion of graphite during sintering, leading to the presence of free graphite in the pores, thus enhancing machinability. Furthermore, a lower amount of graphite in the matrix leads to a lower hardness, which is also beneficial to machinability. It is also found that the presence of copper enhances the diffusion of graphite, through the formation of a liquid phase during sintering. With the objective of improving machinability by reaching higher densities, blends were developed for densification through liquid phase sintering. High density samples are obtained (>7.5 g/cm3) for blends prepared with Fe-C-P constituents, namely with 0.5%P and 2.4%C. By systematically studying the effect of different parameters, the importance of the chemical composition (mainly the carbon content) and the importance of the sintering cycle (particularly the cooling rate) are demonstrated. Moreover, various heat treatments studied illustrate the different microstructures achievable for this system, showing various amounts of cementite, pearlite and free graphite. Although the machinability is limited for samples containing large amounts of cementite, it can be greatly improved with very slow cooling, leading to graphitization of the carbon in presence of phosphorus. Adequate control of the sintering cycle on samples made from FGS1625 powder leads to the obtention of high density (≥7.0 g/cm 3) microstructures containing various amounts of pearlite, ferrite and free graphite. Obtaining ferritic microstructures with free graphite designed for very high machinability (tool wear <1.0%) or fine pearlitic microstructures with excellent mechanical properties (transverse rupture strength >1600 MPa) is therefore possible. These results show that improvement of machinability through higher densities is limited by microstructure. Indeed, for the studied samples, microstructure is dominant in the determination of machinability, far more important than density, judging by the influence of cementite or of the volume fraction of free graphite on machinability for example. (Abstract shortened by UMI.)

Method of Individual Forecasting of Technical State of Logging Machines

NASA Astrophysics Data System (ADS)

Kozlov, V. G.; Gulevsky, V. A.; Skrypnikov, A. V.; Logoyda, V. S.; Menzhulova, A. S.

2018-03-01

Development of the model that evaluates the possibility of failure requires the knowledge of changes’ regularities of technical condition parameters of the machines in use. To study the regularities, the need to develop stochastic models that take into account physical essence of the processes of destruction of structural elements of the machines, the technology of their production, degradation and the stochastic properties of the parameters of the technical state and the conditions and modes of operation arose.

CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

NASA Astrophysics Data System (ADS)

Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

2018-04-01

CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Applying machine learning to identify autistic adults using imitation: An exploratory study.

PubMed

Li, Baihua; Sharma, Arjun; Meng, James; Purushwalkam, Senthil; Gowen, Emma

2017-01-01

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

Calculating Synchronous Inductive Reactances of Contactless Machines When Magnetic Circuit is Saturated and of Machines with Superconducting Excitation Windings,

DTIC Science & Technology

The work studies the effect of magnetic circuit saturation on the synchronous inductive reactance of the armature. A practical method is given for...calculating synchronized parameters in saturating synchronized machines with additional clearances and machines with superconducting excitation windings.

Non-conventional rule of making a periodically varying different-pole magnetic field in low-power alternating current electrical machines with using ring coils in multiphase armature winding

NASA Astrophysics Data System (ADS)

Plastun, A. T.; Tikhonova, O. V.; Malygin, I. V.

2018-02-01

The paper presents methods of making a periodically varying different-pole magnetic field in low-power electrical machines. Authors consider classical designs of electrical machines and machines with ring windings in armature, structural features and calculated parameters of magnetic circuit for these machines.

Parameter estimation using meta-heuristics in systems biology: a comprehensive review.

PubMed

Sun, Jianyong; Garibaldi, Jonathan M; Hodgman, Charlie

2012-01-01

This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

High speed turning of compacted graphite iron using controlled modulation

NASA Astrophysics Data System (ADS)

Stalbaum, Tyler Paul

Compacted graphite iron (CGI) is a material which emerged as a candidate material to replace cast iron (CI) in the automotive industry for engine block castings. Its thermal and mechanical properties allow the CGI-based engines to operate at higher cylinder pressures and temperatures than CI-based engines, allowing for lower fuel emissions and increased fuel economy. However, these same properties together with the thermomechanical wear mode in the CGI-CBN system result in poor machinability and inhibit CGI from seeing wide spread use in the automotive industry. In industry, machining of CGI is done only at low speeds, less than V = 200 m/min, to avoid encountering rapid wear of the cutting tools during cutting. Studies have suggested intermittent cutting operations such as milling suffer less severe tool wear than continuous cutting. Furthermore, evidence that a hard sulfide layer which forms over the cutting edge in machining CI at high speeds is absent during machining CGI is a major factor in the difference in machinability of these material systems. The present study addresses both of these issues by modification to the conventional machining process to allow intermittent continuous cutting. The application of controlled modulation superimposed onto the cutting process -- modulation-assisted machining (MAM) -- is shown to be quite effective in reducing the wear of cubic boron nitride (CBN) tools when machining CGI at high machining speeds (> 500 m/min). The tool life is at least 20 times greater than found in conventional machining of CGI. This significant reduction in wear is a consequence of reduction in the severity of the tool-work contact conditions with MAM. The propensity for thermochemical wear of CBN is thus reduced. It is found that higher cutting speed (> 700 m/min) leads to lower tool wear with MAM. The MAM configuration employing feed-direction modulation appears feasible for implementation at high speeds and offers a solution to this challenging class of industrial machining applications. This study's approach is by series of high speed turning tests of CGI with CBN tools, comparing conventional machining to MAM for similar parameters otherwise, by tool wear measurements and machinability observations.

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

NASA Astrophysics Data System (ADS)

Okokpujie, Imhade Princess; Ikumapayi, Omolayo M.; Okonkwo, Ugochukwu C.; Salawu, Enesi Y.; Afolalu, Sunday A.; Dirisu, Joseph O.; Nwoke, Obinna N.; Ajayi, Oluseyi O.

2017-12-01

In recent machining operation, tool life is one of the most demanding tasks in production process, especially in the automotive industry. The aim of this paper is to study tool wear on HSS in end milling of aluminium 6061 alloy. The experiments were carried out to investigate tool wear with the machined parameters and to developed mathematical model using response surface methodology. The various machining parameters selected for the experiment are spindle speed (N), feed rate (f), axial depth of cut (a) and radial depth of cut (r). The experiment was designed using central composite design (CCD) in which 31 samples were run on SIEG 3/10/0010 CNC end milling machine. After each experiment the cutting tool was measured using scanning electron microscope (SEM). The obtained optimum machining parameter combination are spindle speed of 2500 rpm, feed rate of 200 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.0mm was found out to achieved the minimum tool wear as 0.213 mm. The mathematical model developed predicted the tool wear with 99.7% which is within the acceptable accuracy range for tool wear prediction.

Effects of Process Parameters and Cryotreated Electrode on the Radial Overcut of Aisi 304 IN SiC Powder Mixed Edm

NASA Astrophysics Data System (ADS)

Bhaumik, Munmun; Maity, Kalipada

Powder mixed electro discharge machining (PMEDM) is further advancement of conventional electro discharge machining (EDM) where the powder particles are suspended in the dielectric medium to enhance the machining rate as well as surface finish. Cryogenic treatment is introduced in this process for improving the tool life and cutting tool properties. In the present investigation, the characterization of the cryotreated tempered electrode was performed. An attempt has been made to study the effect of cryotreated double tempered electrode on the radial overcut (ROC) when SiC powder is mixed in the kerosene dielectric during electro discharge machining of AISI 304. The process performance has been evaluated by means of ROC when peak current, pulse on time, gap voltage, duty cycle and powder concentration are considered as process parameters and machining is performed by using tungsten carbide electrodes (untreated and double tempered electrodes). A regression analysis was performed to correlate the data between the response and the process parameters. Microstructural analysis was carried out on the machined surfaces. Least radial overcut was observed for conventional EDM as compared to powder mixed EDM. Cryotreated double tempered electrode significantly reduced the radial overcut than untreated electrode.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

PubMed Central

Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-01-01

Background To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets. Objective This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. Methods This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. Results We are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022. Conclusions Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. PMID:28851678

Forming Completely Penetrated Welded T-joints when Pulsed Arc Welding

NASA Astrophysics Data System (ADS)

Krampit, N. Yu; Krampit, M. A.; Sapozhkov, A. S.

2016-04-01

The paper is focused on revealing the influence of welding parameters on weld formation when pulsed arc welding. As an experimental sample a T-joint over 10 mm was selected. Welding was carried out in flat position, which required no edge preparation but provided mono-directional guaranteed root penetration. The following parameters of welding were subjected to investigation: gap in the joint, wire feed rate and incline angles of the torch along and across the weld axis. Technological recommendations have been made with respect to pulsed arc welding; the cost price of product manufacturing can be reduced on their basis due to reduction of labor input required by machining, lowering consumption of welding materials and electric power.

Manufacturing Technology Support (MATES). Task Order 0021: Air Force Technology and Industrial Base Research and Analysis, Subtask Order 06: Direct Digital Manufacturing

DTIC Science & Technology

2011-08-01

industries and key players providing equipment include Flow and OMAX. The decision tree for waterjet machining is shown in Figure 28. Figure 28...about the melt pool. Process parameters including powder flow , laser power, and scan speed are adjusted accordingly • Multiple materials o BD...project.eu.com/home/home_page_static.jsp o Working with multiple partners; one is Cochlear . Using LMD or SLM to fabricate cochlear implants with 10

Influence of electrical resistivity and machining parameters on electrical discharge machining performance of engineering ceramics.

PubMed

Ji, Renjie; Liu, Yonghong; Diao, Ruiqiang; Xu, Chenchen; Li, Xiaopeng; Cai, Baoping; Zhang, Yanzhen

2014-01-01

Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM) is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical discharge machining ZnO/Al2O3 ceramic.

The study of optimization on process parameters of high-accuracy computerized numerical control polishing

NASA Astrophysics Data System (ADS)

Huang, Wei-Ren; Huang, Shih-Pu; Tsai, Tsung-Yueh; Lin, Yi-Jyun; Yu, Zong-Ru; Kuo, Ching-Hsiang; Hsu, Wei-Yao; Young, Hong-Tsu

2017-09-01

Spherical lenses lead to forming spherical aberration and reduced optical performance. Consequently, in practice optical system shall apply a combination of spherical lenses for aberration correction. Thus, the volume of the optical system increased. In modern optical systems, aspherical lenses have been widely used because of their high optical performance with less optical components. However, aspherical surfaces cannot be fabricated by traditional full aperture polishing process due to their varying curvature. Sub-aperture computer numerical control (CNC) polishing is adopted for aspherical surface fabrication in recent years. By using CNC polishing process, mid-spatial frequency (MSF) error is normally accompanied during this process. And the MSF surface texture of optics decreases the optical performance for high precision optical system, especially for short-wavelength applications. Based on a bonnet polishing CNC machine, this study focuses on the relationship between MSF surface texture and CNC polishing parameters, which include feed rate, head speed, track spacing and path direction. The power spectral density (PSD) analysis is used to judge the MSF level caused by those polishing parameters. The test results show that controlling the removal depth of single polishing path, through the feed rate, and without same direction polishing path for higher total removal depth can efficiently reduce the MSF error. To verify the optical polishing parameters, we divided a correction polishing process to several polishing runs with different direction polishing paths. Compare to one shot polishing run, multi-direction path polishing plan could produce better surface quality on the optics.

Application of Fuzzy TOPSIS for evaluating machining techniques using sustainability metrics

NASA Astrophysics Data System (ADS)

Digalwar, Abhijeet K.

2018-04-01

Sustainable processes and techniques are getting increased attention over the last few decades due to rising concerns over the environment, improved focus on productivity and stringency in environmental as well as occupational health and safety norms. The present work analyzes the research on sustainable machining techniques and identifies techniques and parameters on which sustainability of a process is evaluated. Based on the analysis these parameters are then adopted as criteria’s to evaluate different sustainable machining techniques such as Cryogenic Machining, Dry Machining, Minimum Quantity Lubrication (MQL) and High Pressure Jet Assisted Machining (HPJAM) using a fuzzy TOPSIS framework. In order to facilitate easy arithmetic, the linguistic variables represented by fuzzy numbers are transformed into crisp numbers based on graded mean representation. Cryogenic machining was found to be the best alternative sustainable technique as per the fuzzy TOPSIS framework adopted. The paper provides a method to deal with multi criteria decision making problems in a complex and linguistic environment.

Speed-Selector Guard For Machine Tool

NASA Technical Reports Server (NTRS)

Shakhshir, Roda J.; Valentine, Richard L.

1992-01-01

Simple guardplate prevents accidental reversal of direction of rotation or sudden change of speed of lathe, milling machine, or other machine tool. Custom-made for specific machine and control settings. Allows control lever to be placed at only one setting. Operator uses handle to slide guard to engage or disengage control lever. Protects personnel from injury and equipment from damage occurring if speed- or direction-control lever inadvertently placed in wrong position.

Scale effects and a method for similarity evaluation in micro electrical discharge machining

NASA Astrophysics Data System (ADS)

Liu, Qingyu; Zhang, Qinhe; Wang, Kan; Zhu, Guang; Fu, Xiuzhuo; Zhang, Jianhua

2016-08-01

Electrical discharge machining(EDM) is a promising non-traditional micro machining technology that offers a vast array of applications in the manufacturing industry. However, scale effects occur when machining at the micro-scale, which can make it difficult to predict and optimize the machining performances of micro EDM. A new concept of "scale effects" in micro EDM is proposed, the scale effects can reveal the difference in machining performances between micro EDM and conventional macro EDM. Similarity theory is presented to evaluate the scale effects in micro EDM. Single factor experiments are conducted and the experimental results are analyzed by discussing the similarity difference and similarity precision. The results show that the output results of scale effects in micro EDM do not change linearly with discharge parameters. The values of similarity precision of machining time significantly increase when scaling-down the capacitance or open-circuit voltage. It is indicated that the lower the scale of the discharge parameter, the greater the deviation of non-geometrical similarity degree over geometrical similarity degree, which means that the micro EDM system with lower discharge energy experiences more scale effects. The largest similarity difference is 5.34 while the largest similarity precision can be as high as 114.03. It is suggested that the similarity precision is more effective in reflecting the scale effects and their fluctuation than similarity difference. Consequently, similarity theory is suitable for evaluating the scale effects in micro EDM. This proposed research offers engineering values for optimizing the machining parameters and improving the machining performances of micro EDM.

Substantiation of basic scheme of grain cleaning machine for preparation of agricultural crops seeds

NASA Astrophysics Data System (ADS)

Giyevskiy, A. M.; Orobinsky, V. I.; Tarasenko, A. P.; Chernyshov, A. V.; Kurilov, D. O.

2018-03-01

The article presents data on the feasibility of the concept of a high-efficiency seed cleaner with the consistent use of the air flow in aspiration and the multi-tier placement of the sorting grids in grating mills. As a result of modeling, the directions for further improvement of air-screen seed cleaning machines have been identified: an increase in the proportion of sorting grids in the mills up to 70 ... 80% and an increase in the speed of the air flow in the channel of the pre-filter cleaning up to 8.0 m / s. Experiments have established the competence of using mathematical modeling of airflow in the pneumatic system with the use of a finite-volume method for solving hydrodynamic equations for substantiating the basic parameters of the pneumatic system.

A sustainable solid state recycling of pure aluminum by means of friction stir extrusion process (FSE)

NASA Astrophysics Data System (ADS)

Mehtedi, Mohamad El; Forcellese, Archimede; Simoncini, Michela; Spigarelli, Stefano

2018-05-01

In this research, the feasibility of solid-state recycling of pure aluminum AA1099 machining chips using FSE process is investigated. In the early stage, a FE simulation was conducted in order to optimize the die design and the process parameters in terms of plunge rotational speed and extrusion rate. The AA1099 aluminum chips were produced by turning of an as-received bar without lubrication. The chips were compacted on a MTS machine up to 150KN of load. The extruded samples were analyzed by optical and electron microscope in order to see the material flow and to characterize the microstructure. Finally, micro-hardness Vickers profiles were carried out, in both longitudinal and transversal direction of the obtained profiles, in order to investigate the homogeneity of the mechanical properties of the extrudate.

In-situ acoustic signature monitoring in additive manufacturing processes

NASA Astrophysics Data System (ADS)

Koester, Lucas W.; Taheri, Hossein; Bigelow, Timothy A.; Bond, Leonard J.; Faierson, Eric J.

2018-04-01

Additive manufacturing is a rapidly maturing process for the production of complex metallic, ceramic, polymeric, and composite components. The processes used are numerous, and with the complex geometries involved this can make quality control and standardization of the process and inspection difficult. Acoustic emission measurements have been used previously to monitor a number of processes including machining and welding. The authors have identified acoustic signature measurement as a potential means of monitoring metal additive manufacturing processes using process noise characteristics and those discrete acoustic emission events characteristic of defect growth, including cracks and delamination. Results of acoustic monitoring for a metal additive manufacturing process (directed energy deposition) are reported. The work investigated correlations between acoustic emissions and process noise with variations in machine state and deposition parameters, and provided proof of concept data that such correlations do exist.

Effects of cutting parameters and machining environments on surface roughness in hard turning using design of experiment

NASA Astrophysics Data System (ADS)

Mia, Mozammel; Bashir, Mahmood Al; Dhar, Nikhil Ranjan

2016-07-01

Hard turning is gradually replacing the time consuming conventional turning process, which is typically followed by grinding, by producing surface quality compatible to grinding. The hard turned surface roughness depends on the cutting parameters, machining environments and tool insert configurations. In this article the variation of the surface roughness of the produced surfaces with the changes in tool insert configuration, use of coolant and different cutting parameters (cutting speed, feed rate) has been investigated. This investigation was performed in machining AISI 1060 steel, hardened to 56 HRC by heat treatment, using coated carbide inserts under two different machining environments. The depth of cut, fluid pressure and material hardness were kept constant. The Design of Experiment (DOE) was performed to determine the number and combination sets of different cutting parameters. A full factorial analysis has been performed to examine the effect of main factors as well as interaction effect of factors on surface roughness. A statistical analysis of variance (ANOVA) was employed to determine the combined effect of cutting parameters, environment and tool configuration. The result of this analysis reveals that environment has the most significant impact on surface roughness followed by feed rate and tool configuration respectively.

Experimental Investigation and Optimization of Response Variables in WEDM of Inconel - 718

NASA Astrophysics Data System (ADS)

Karidkar, S. S.; Dabade, U. A.

2016-02-01

Effective utilisation of Wire Electrical Discharge Machining (WEDM) technology is challenge for modern manufacturing industries. Day by day new materials with high strengths and capabilities are being developed to fulfil the customers need. Inconel - 718 is similar kind of material which is extensively used in aerospace applications, such as gas turbine, rocket motors, and spacecraft as well as in nuclear reactors and pumps etc. This paper deals with the experimental investigation of optimal machining parameters in WEDM for Surface Roughness, Kerf Width and Dimensional Deviation using DoE such as Taguchi methodology, L9 orthogonal array. By keeping peak current constant at 70 A, the effect of other process parameters on above response variables were analysed. Obtained experimental results were statistically analysed using Minitab-16 software. Analysis of Variance (ANOVA) shows pulse on time as the most influential parameter followed by wire tension whereas spark gap set voltage is observed to be non-influencing parameter. Multi-objective optimization technique, Grey Relational Analysis (GRA), shows optimal machining parameters such as pulse on time 108 Machine unit, spark gap set voltage 50 V and wire tension 12 gm for optimal response variables considered for the experimental analysis.

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers.

PubMed

Gonzalez Viejo, Claudia; Fuentes, Sigfredo; Torrico, Damir D; Howell, Kate; Dunshea, Frank R

2018-05-01

Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of sensory descriptors in beer using the physical measurements of color and foam-related parameters. A robotic pourer (RoboBEER), was used to obtain 15 color and foam-related parameters from 22 different commercial beer samples. A sensory session using quantitative descriptive analysis (QDA ® ) with trained panelists was conducted to assess the intensity of 10 beer descriptors. Results showed that the principal component analysis explained 64% of data variability with correlations found between foam-related descriptors from sensory and RoboBEER such as the positive and significant correlation between carbon dioxide and carbonation mouthfeel (R = 0.62), correlation of viscosity to sensory, and maximum volume of foam and total lifetime of foam (R = 0.75, R = 0.77, respectively). Using the RoboBEER parameters as inputs, an artificial neural network (ANN) regression model showed high correlation (R = 0.91) to predict the intensity levels of 10 related sensory descriptors such as yeast, grains and hops aromas, hops flavor, bitter, sour and sweet tastes, viscosity, carbonation, and astringency. This paper is a novel approach for food science using machine modeling techniques that could contribute significantly to rapid screenings of food and brewage products for the food industry and the implementation of Artificial Intelligence (AI). The use of RoboBEER to assess beer quality showed to be a reliable, objective, accurate, and less time-consuming method to predict sensory descriptors compared to trained sensory panels. Hence, this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line for industry applications. © 2018 Institute of Food Technologists®.

National Synchrotron Light Source annual report 1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hulbert, S.L.; Lazarz, N.M.

1992-04-01

This report discusses the following research conducted at NSLS: atomic and molecular science; energy dispersive diffraction; lithography, microscopy and tomography; nuclear physics; UV photoemission and surface science; x-ray absorption spectroscopy; x-ray scattering and crystallography; x-ray topography; workshop on surface structure; workshop on electronic and chemical phenomena at surfaces; workshop on imaging; UV FEL machine reviews; VUV machine operations; VUV beamline operations; VUV storage ring parameters; x-ray machine operations; x-ray beamline operations; x-ray storage ring parameters; superconducting x-ray lithography source; SXLS storage ring parameters; the accelerator test facility; proposed UV-FEL user facility at the NSLS; global orbit feedback systems; and NSLSmore » computer system.« less

Warpage optimization on a mobile phone case using response surface methodology (RSM)

NASA Astrophysics Data System (ADS)

Lee, X. N.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.; Shazzuan, S.

2017-09-01

Plastic injection moulding is a popular manufacturing method not only it is reliable, but also efficient and cost saving. It able to produce plastic part with detailed features and complex geometry. However, defects in injection moulding process degrades the quality and aesthetic of the injection moulded product. The most common defect occur in the process is warpage. Inappropriate process parameter setting of injection moulding machine is one of the reason that leads to the occurrence of warpage. The aims of this study were to improve the quality of injection moulded part by investigating the optimal parameters in minimizing warpage using Response Surface Methodology (RSM). Subsequent to this, the most significant parameter was identified and recommended parameters setting was compared with the optimized parameter setting using RSM. In this research, the mobile phone case was selected as case study. The mould temperature, melt temperature, packing pressure, packing time and cooling time were selected as variables whereas warpage in y-direction was selected as responses in this research. The simulation was carried out by using Autodesk Moldflow Insight 2012. In addition, the RSM was performed by using Design Expert 7.0. The warpage in y direction recommended by RSM were reduced by 70 %. RSM performed well in solving warpage issue.

Tectonic environments of South American porphyry copper magmatism through time revealed by spatiotemporal data mining

NASA Astrophysics Data System (ADS)

Butterworth, N.; Steinberg, D.; Müller, R. D.; Williams, S.; Merdith, A. S.; Hardy, S.

2016-12-01

Porphyry ore deposits are known to be associated with arc magmatism on the overriding plate at subduction zones. While general mechanisms for driving magmatism are well established, specific subduction-related parameters linking episodes of ore deposit formation to specific tectonic environments have only been qualitatively inferred and have not been formally tested. We develop a four-dimensional approach to reconstruct age-dated ore deposits, with the aim of isolating the tectonomagmatic parameters leading to the formation of copper deposits during subduction. We use a plate tectonic model with continuously closing plate boundaries, combined with reconstructions of the spatiotemporal distribution of the ocean floor, including subducted portions of the Nazca/Farallon plates. The models compute convergence rates and directions, as well as the age of the downgoing plate through time. To identify and quantify tectonic parameters that are robust predictors of Andean porphyry copper magmatism and ore deposit formation, we test two alternative supervised machine learning methods; the "random forest" (RF) ensemble and "support vector machines" (SVM). We find that a combination of rapid convergence rates ( 100 km/Myr), subduction obliquity of 15°, a subducting plate age between 25-70 Myr old, and a location far from the subducting trench boundary (>2000 km) represents favorable conditions for porphyry magmatism and related ore deposits to occur. These parameters are linked to the availability of oceanic sediments, the changing small-scale convection around the subduction zone, and the availability of the partial melt in the mantle wedge. When coupled, these parameters could influence the genesis and exhumation of porphyry copper deposits.

Machine-Thermal Coupling Stresses Analysis of the Fin-Type Structural Thermoelectric Generator

NASA Astrophysics Data System (ADS)

Zhang, Zheng; Yue, Hao; Chen, Dongbo; Qin, Delei; Chen, Zijian

2017-05-01

The design structure and heat-transfer mechanism of a thermoelectric generator (TEG) determine its body temperature state. Thermal stress and thermal deformation generated by the temperature variation directly affect the stress state of thermoelectric modules (TEMs). Therefore, the rated temperature and pressing force of TEMs are important parameters in TEG design. Here, the relationships between structural of a fin-type TEG (FTEG) and these parameters are studied by modeling and "machine-thermal" coupling simulation. An indirect calculation method is adopted in the coupling simulation. First, numerical heat transfer calculations of a three-dimensional FTEG model are conducted according to an orthogonal simulation table. The influences of structural parameters for heat transfer in the channel and outer fin temperature distribution are analyzed. The optimal structural parameters are obtained and used to simulate temperature field of the outer fins. Second, taking the thermal calculation results as the initial condition, the thermal-solid coupling calculation is adopted. The thermal stresses of outer fin, mechanical force of spring-angle pressing mechanism, and clamping force on a TEM are analyzed. The simulation results show that the heat transfer area of the inner fin and the physical parameters of the metal materials are the keys to determining the FTEG temperature field. The pressing mechanism's mechanical force can be reduced by reducing the outer fin angle. In addition, a corrugated cooling water pipe, which has cooling and spring functionality, is conducive to establishing an adaptable clamping force to avoid the TEMs being crushed by the thermal stresses in the body.

Lean energy analysis of CNC lathe

NASA Astrophysics Data System (ADS)

Liana, N. A.; Amsyar, N.; Hilmy, I.; Yusof, MD

2018-01-01

The industrial sector in Malaysia is one of the main sectors that have high percentage of energy demand compared to other sector and this problem may lead to the future power shortage and increasing the production cost of a company. Suitable initiatives should be implemented by the industrial sectors to solve the issues such as by improving the machining system. In the past, the majority of the energy consumption in industry focus on lighting, HVAC and office section usage. Future trend, manufacturing process is also considered to be included in the energy analysis. A study on Lean Energy Analysis in a machining process is presented. Improving the energy efficiency in a lathe machine by enhancing the cutting parameters of turning process is discussed. Energy consumption of a lathe machine was analyzed in order to identify the effect of cutting parameters towards energy consumption. It was found that the combination of parameters for third run (spindle speed: 1065 rpm, depth of cut: 1.5 mm, feed rate: 0.3 mm/rev) was the most preferred and ideal to be used during the turning machining process as it consumed less energy usage.

P09.62 Towards individualized survival prediction in glioblastoma patients using machine learning methods

PubMed Central

Vera, L.; Pérez-Beteta, J.; Molina, D.; Borrás, J. M.; Benavides, M.; Barcia, J. A.; Velásquez, C.; Albillo, D.; Lara, P.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Machine learning methods are integrated in clinical research studies due to their strong capability to discover parameters having a high information content and their predictive combined potential. Several studies have been developed using glioblastoma patient’s imaging data. Many of them have focused on including large numbers of variables, mostly two-dimensional textural features and/or genomic data, regardless of their meaning or potential clinical relevance. Materials and methods: 193 glioblastoma patients were included in the study. Preoperative 3D magnetic resonance images were collected and semi-automatically segmented using an in-house software. After segmentation, a database of 90 parameters including geometrical and textural image-based measures together with patients’ clinical data (including age, survival, type of treatment, etc.) was constructed. The criterion for including variables in the study was that they had either shown individual impact on survival in single or multivariate analyses or have a precise clinical or geometrical meaning. These variables were used to perform several machine learning experiments. In a first set of computational cross-validation experiments based on regression trees, those attributes showing the highest information measures were extracted. In the second phase, more sophisticated learning methods were employed in order to validate the potential of the previous variables predicting survival. Concretely support vector machines, neural networks and sparse grid methods were used. Results: Variables showing high information measure in the first phase provided the best prediction results in the second phase. Specifically, patient age, Stupp regimen and a geometrical measure related with the irregularity of contrast-enhancing areas were the variables showing the highest information measure in the first stage. For the second phase, the combinations of patient age and Stupp regimen together with one tumor geometrical measure and one tumor heterogeneity feature reached the best quality prediction. Conclusions: Advanced machine learning methods identified the parameters with the highest information measure and survival predictive potential. The uninformed machine learning methods identified a novel feature measure with direct impact on survival. Used in combination with other previously known variables multi-indexes can be defined that can help in tumor characterization and prognosis prediction. Recent advances on the definition of those multi-indexes will be reported in the conference. Funding: James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Collaborative award 220020450 and planning grant 220020420], MINECO/FEDER [MTM2015-71200-R], JCCM [PEII-2014-031-P].

Effect of processing parameters on surface finish for fused deposition machinable wax patterns

NASA Technical Reports Server (NTRS)

Roberts, F. E., III

1995-01-01

This report presents a study on the effect of material processing parameters used in layer-by-layer material construction on the surface finish of a model to be used as an investment casting pattern. The data presented relate specifically to fused deposition modeling using a machinable wax.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

NASA Astrophysics Data System (ADS)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Deploying response surface methodology (RSM) and glowworm swarm optimization (GSO) in optimizing warpage on a mobile phone cover

NASA Astrophysics Data System (ADS)

Lee, X. N.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.; Shazzuan, S.

2017-09-01

Plastic injection moulding is a popular manufacturing method not only it is reliable, but also efficient and cost saving. It able to produce plastic part with detailed features and complex geometry. However, defects in injection moulding process degrades the quality and aesthetic of the injection moulded product. The most common defect occur in the process is warpage. Inappropriate process parameter setting of injection moulding machine is one of the reason that leads to the occurrence of warpage. The aims of this study were to improve the quality of injection moulded part by investigating the optimal parameters in minimizing warpage using Response Surface Methodology (RSM) and Glowworm Swarm Optimization (GSO). Subsequent to this, the most significant parameter was identified and recommended parameters setting was compared with the optimized parameter setting using RSM and GSO. In this research, the mobile phone case was selected as case study. The mould temperature, melt temperature, packing pressure, packing time and cooling time were selected as variables whereas warpage in y-direction was selected as responses in this research. The simulation was carried out by using Autodesk Moldflow Insight 2012. In addition, the RSM was performed by using Design Expert 7.0 whereas the GSO was utilized by using MATLAB. The warpage in y direction recommended by RSM were reduced by 70 %. The warpages recommended by GSO were decreased by 61 % in y direction. The resulting warpages under optimal parameter setting by RSM and GSO were validated by simulation in AMI 2012. RSM performed better than GSO in solving warpage issue.

Performance Analysis of Three-Phase Induction Motor with AC Direct and VFD

NASA Astrophysics Data System (ADS)

Kumar, Dinesh

2018-03-01

The electrical machine analysis and performance calculation is a very important aspect of efficient drive system design. The development of power electronics devices and power converters provide smooth speed control of Induction Motors by changing the frequency of input supply. These converters, on one hand are providing a more flexible speed control that also leads to problems of harmonics and their associated ailments like pulsating torque, distorted current and voltage waveforms, increasing losses etc. This paper includes the performance analysis of three phase induction motor with three-phase AC direct and variable frequency drives (VFD). The comparison has been concluded with respect to various parameters. MATLAB-SIMULINKTM is used for the analysis.

Nonlinear control of voltage source converters in AC-DC power system.

PubMed

Dash, P K; Nayak, N

2014-07-01

This paper presents the design of a robust nonlinear controller for a parallel AC-DC power system using a Lyapunov function-based sliding mode control (LYPSMC) strategy. The inputs for the proposed control scheme are the DC voltage and reactive power errors at the converter station and the active and reactive power errors at the inverter station of the voltage-source converter-based high voltage direct current transmission (VSC-HVDC) link. The stability and robust tracking of the system parameters are ensured by applying the Lyapunov direct method. Also the gains of the sliding mode control (SMC) are made adaptive using the stability conditions of the Lyapunov function. The proposed control strategy offers invariant stability to a class of systems having modeling uncertainties due to parameter changes and exogenous inputs. Comprehensive computer simulations are carried out to verify the proposed control scheme under several system disturbances like changes in short-circuit ratio, converter parametric changes, and faults on the converter and inverter buses for single generating system connected to the power grid in a single machine infinite-bus AC-DC network and also for a 3-machine two-area power system. Furthermore, a second order super twisting sliding mode control scheme has been presented in this paper that provides a higher degree of nonlinearity than the LYPSMC and damps faster the converter and inverter voltage and power oscillations. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Theoretical study of cut area of reduction of large surfaces of rotation parts on machines with rotary cutters “Extra”

NASA Astrophysics Data System (ADS)

Bondarenko, J. A.; Fedorenko, M. A.; Pogonin, A. A.

2018-03-01

Large parts can be treated without disassembling machines using “Extra”, having technological and design challenges, which differ from the challenges in the processing of these components on the stationary machine. Extension machines are used to restore large parts up to the condition allowing one to use them in a production environment. To achieve the desired accuracy and surface roughness parameters, the surface after rotary grinding becomes recoverable, which greatly increases complexity. In order to improve production efficiency and productivity of the process, the qualitative rotary processing of the machined surface is applied. The rotary cutting process includes a continuous change of the cutting edge surfaces. The kinematic parameters of a rotary cutting define its main features and patterns, the cutting operation of the rotary cutting instrument.

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability

NASA Astrophysics Data System (ADS)

Ravela, S.

2017-12-01

In this presentation we review the two directions of a symbiotic relationship between machine learning and the geosciences in relation to prediction and predictability. In the first direction, we develop ensemble, information theoretic and manifold learning framework to adaptively improve state and parameter estimates in nonlinear high-dimensional non-Gaussian problems, showing in particular that tractable variational approaches can be produced. We demonstrate these applications in the context of autonomous mapping of environmental coherent structures and other idealized problems. In the reverse direction, we show that data assimilation, particularly probabilistic approaches for filtering and smoothing offer a novel and useful way to train neural networks, and serve as a better basis than gradient based approaches when we must quantify uncertainty in association with nonlinear, chaotic processes. In many inference problems in geosciences we seek to build reduced models to characterize local sensitivies, adjoints or other mechanisms that propagate innovations and errors. Here, the particular use of neural approaches for such propagation trained using ensemble data assimilation provides a novel framework. Through these two examples of inference problems in the earth sciences, we show that not only is learning useful to broaden existing methodology, but in reverse, geophysical methodology can be used to influence paradigms in learning.

Modeling and Analysis of High Torque Density Transverse Flux Machines for Direct-Drive Applications

NASA Astrophysics Data System (ADS)

Hasan, Iftekhar

Commercially available permanent magnet synchronous machines (PMSM) typically use rare-earth-based permanent magnets (PM). However, volatility and uncertainty associated with the supply and cost of rare-earth magnets have caused a push for increased research into the development of non-rare-earth based PM machines and reluctance machines. Compared to other PMSM topologies, the Transverse Flux Machine (TFM) is a promising candidate to get higher torque densities at low speed for direct-drive applications, using non-rare-earth based PMs. The TFMs can be designed with a very small pole pitch which allows them to attain higher force density than conventional radial flux machines (RFM) and axial flux machines (AFM). This dissertation presents the modeling, electromagnetic design, vibration analysis, and prototype development of a novel non-rare-earth based PM-TFM for a direct-drive wind turbine application. The proposed TFM addresses the issues of low power factor, cogging torque, and torque ripple during the electromagnetic design phase. An improved Magnetic Equivalent Circuit (MEC) based analytical model was developed as an alternative to the time-consuming 3D Finite Element Analysis (FEA) for faster electromagnetic analysis of the TFM. The accuracy and reliability of the MEC model were verified, both with 3D-FEA and experimental results. The improved MEC model was integrated with a Particle Swarm Optimization (PSO) algorithm to further enhance the capability of the analytical tool for performing rigorous optimization of performance-sensitive machine design parameters to extract the highest torque density for rated speed. A novel concept of integrating the rotary transformer within the proposed TFM design was explored to completely eliminate the use of magnets from the TFM. While keeping the same machine envelope, and without changing the stator or rotor cores, the primary and secondary of a rotary transformer were embedded into the double-sided TFM. The proposed structure allowed for improved flux-weakening capabilities of the TFM for wide speed operations. The electromagnetic design feature of stator pole shaping was used to address the issue of cogging torque and torque ripple in 3-phase TFM. The slant-pole tooth-face in the stator showed significant improvements in cogging torque and torque ripple performance during the 3-phase FEA analysis of the TFM. A detailed structural analysis for the proposed TFM was done prior to the prototype development to validate the structural integrity of the TFM design at rated and maximum speed operation. Vibration performance of the TFM was investigated to determine the structural performance of the TFM under resonance. The prototype for the proposed TFM was developed at the Alternative Energy Laboratory of the University of Akron. The working prototype is a testament to the feasibility of developing and implementing the novel TFM design proposed in this research. Experiments were performed to validate the 3D-FEA electromagnetic and vibration performance result.

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

National Synchrotron Light Source annual report 1991. Volume 1, October 1, 1990--September 30, 1991

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hulbert, S.L.; Lazarz, N.M.

1992-04-01

This report discusses the following research conducted at NSLS: atomic and molecular science; energy dispersive diffraction; lithography, microscopy and tomography; nuclear physics; UV photoemission and surface science; x-ray absorption spectroscopy; x-ray scattering and crystallography; x-ray topography; workshop on surface structure; workshop on electronic and chemical phenomena at surfaces; workshop on imaging; UV FEL machine reviews; VUV machine operations; VUV beamline operations; VUV storage ring parameters; x-ray machine operations; x-ray beamline operations; x-ray storage ring parameters; superconducting x-ray lithography source; SXLS storage ring parameters; the accelerator test facility; proposed UV-FEL user facility at the NSLS; global orbit feedback systems; and NSLSmore » computer system.« less

Development of the FITS tools package for multiple software environments

NASA Technical Reports Server (NTRS)

Pence, W. D.; Blackburn, J. K.

1992-01-01

The HEASARC is developing a package of general purpose software for analyzing data files in FITS format. This paper describes the design philosophy which makes the software both machine-independent (it runs on VAXs, Suns, and DEC-stations) and software environment-independent. Currently the software can be compiled and linked to produce IRAF tasks, or alternatively, the same source code can be used to generate stand-alone tasks using one of two implementations of a user-parameter interface library. The machine independence of the software is achieved by writing the source code in ANSI standard Fortran or C, using the machine-independent FITSIO subroutine interface for all data file I/O, and using a standard user-parameter subroutine interface for all user I/O. The latter interface is based on the Fortran IRAF Parameter File interface developed at STScI. The IRAF tasks are built by linking to the IRAF implementation of this parameter interface library. Two other implementations of this parameter interface library, which have no IRAF dependencies, are now available which can be used to generate stand-alone executable tasks. These stand-alone tasks can simply be executed from the machine operating system prompt either by supplying all the task parameters on the command line or by entering the task name after which the user will be prompted for any required parameters. A first release of this FTOOLS package is now publicly available. The currently available tasks are described, along with instructions on how to obtain a copy of the software.

Initial planetary base construction techniques and machine implementation

NASA Technical Reports Server (NTRS)

Crockford, William W.

1987-01-01

Conceptual designs of (1) initial planetary base structures, and (2) an unmanned machine to perform the construction of these structures using materials local to the planet are presented. Rock melting is suggested as a possible technique to be used by the machine in fabricating roads, platforms, and interlocking bricks. Identification of problem areas in machine design and materials processing is accomplished. The feasibility of the designs is contingent upon favorable results of an analysis of the engineering behavior of the product materials. The analysis requires knowledge of several parameters for solution of the constitutive equations of the theory of elasticity. An initial collection of these parameters is presented which helps to define research needed to perform a realistic feasibility study. A qualitative approach to estimating power and mass lift requirements for the proposed machine is used which employs specifications of currently available equipment. An initial, unmanned mission scenario is discussed with emphasis on identifying uncompleted tasks and suggesting design considerations for vehicles and primitive structures which use the products of the machine processing.

The Fluid Foil: The Seventh Simple Machine

ERIC Educational Resources Information Center

Mitts, Charles R.

2012-01-01

A simple machine does one of two things: create a mechanical advantage (lever) or change the direction of an applied force (pulley). Fluid foils are unique among simple machines because they not only change the direction of an applied force (wheel and axle); they convert fluid energy into mechanical energy (wind and Kaplan turbines) or vice versa,…

Relationship of goat milk flow emission variables with milking routine, milking parameters, milking machine characteristics and goat physiology.

PubMed

Romero, G; Panzalis, R; Ruegg, P

2017-11-01

The aim of this paper was to study the relationship between milk flow emission variables recorded during milking of dairy goats with variables related to milking routine, goat physiology, milking parameters and milking machine characteristics, to determine the variables affecting milking performance and help the goat industry pinpoint farm and milking practices that improve milking performance. In total, 19 farms were visited once during the evening milking. Milking parameters (vacuum level (VL), pulsation ratio and pulsation rate, vacuum drop), milk emission flow variables (milking time, milk yield, maximum milk flow (MMF), average milk flow (AVMF), time until 500 g/min milk flow is established (TS500)), doe characteristics of 8 to 10 goats/farm (breed, days in milk and parity), milking practices (overmilking, overstripping, pre-lag time) and milking machine characteristics (line height, presence of claw) were recorded on every farm. The relationships between recorded variables and farm were analysed by a one-way ANOVA analysis. The relationships of milk yield, MMF, milking time and TS500 with goat physiology, milking routine, milking parameters and milking machine design were analysed using a linear mixed model, considering the farm as the random effect. Farm was significant (P<0.05) in all the studied variables. Milk emission flow variables were similar to those recommended in scientific studies. Milking parameters were adequate in most of the farms, being similar to those recommended in scientific studies. Few milking parameters and milking machine characteristics affected the tested variables: average vacuum level only showed tendency on MMF, and milk pipeline height on TS500. Milk yield (MY) was mainly affected by parity, as the interaction of days in milk with parity was also significant. Milking time was mainly affected by milk yield and breed. Also significant were parity, the interaction of days in milk with parity and overstripping, whereas overmilking showed a slight tendency. We concluded that most of the studied variables were mainly related to goat physiology characteristics, as the effects of milking parameters and milking machine characteristics were scarce.

Unsupervised learning of structure in spectroscopic cubes

NASA Astrophysics Data System (ADS)

Araya, M.; Mendoza, M.; Solar, M.; Mardones, D.; Bayo, A.

2018-07-01

We consider the problem of analyzing the structure of spectroscopic cubes using unsupervised machine learning techniques. We propose representing the target's signal as a homogeneous set of volumes through an iterative algorithm that separates the structured emission from the background while not overestimating the flux. Besides verifying some basic theoretical properties, the algorithm is designed to be tuned by domain experts, because its parameters have meaningful values in the astronomical context. Nevertheless, we propose a heuristic to automatically estimate the signal-to-noise ratio parameter of the algorithm directly from data. The resulting light-weighted set of samples (≤ 1% compared to the original data) offer several advantages. For instance, it is statistically correct and computationally inexpensive to apply well-established techniques of the pattern recognition and machine learning domains; such as clustering and dimensionality reduction algorithms. We use ALMA science verification data to validate our method, and present examples of the operations that can be performed by using the proposed representation. Even though this approach is focused on providing faster and better analysis tools for the end-user astronomer, it also opens the possibility of content-aware data discovery by applying our algorithm to big data.

Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.

PubMed

Wang, Jun; Deng, Zhaohong; Luo, Xiaoqing; Jiang, Yizhang; Wang, Shitong

2016-06-01

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε-insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Design optimization for permanent magnet machine with efficient slot per pole ratio

NASA Astrophysics Data System (ADS)

Potnuru, Upendra Kumar; Rao, P. Mallikarjuna

2018-04-01

This paper presents a methodology for the enhancement of a Brush Less Direct Current motor (BLDC) with 6Poles and 8slots. In particular; it is focused on amulti-objective optimization using a Genetic Algorithmand Grey Wolf Optimization developed in MATLAB. The optimization aims to maximize the maximum output power value and minimize the total losses of a motor. This paper presents an application of the MATLAB optimization algorithms to brushless DC (BLDC) motor design, with 7 design parameters chosen to be free. The optimal design parameters of the motor derived by GA are compared with those obtained by Grey Wolf Optimization technique. A comparative report on the specified enhancement approaches appearsthat Grey Wolf Optimization technique has a better convergence.

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

PubMed

Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

2013-12-01

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

PubMed Central

Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-01-01

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization. PMID:28599282

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

PubMed

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Influence of Electrical Resistivity and Machining Parameters on Electrical Discharge Machining Performance of Engineering Ceramics

PubMed Central

Ji, Renjie; Liu, Yonghong; Diao, Ruiqiang; Xu, Chenchen; Li, Xiaopeng; Cai, Baoping; Zhang, Yanzhen

2014-01-01

Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM) is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical discharge machining ZnO/Al2O3 ceramic. PMID:25364912

Verification of directed self-assembly (DSA) guide patterns through machine learning

NASA Astrophysics Data System (ADS)

Shim, Seongbo; Cai, Sibo; Yang, Jaewon; Yang, Seunghune; Choi, Byungil; Shin, Youngsoo

2015-03-01

Verification of full-chip DSA guide patterns (GPs) through simulations is not practical due to long runtime. We develop a decision function (or functions), which receives n geometry parameters of a GP as inputs and predicts whether the GP faithfully produces desired contacts (good) or not (bad). We take a few sample GPs to construct the function; DSA simulations are performed for each GP to decide whether it is good or bad, and the decision is marked in n-dimensional space. The hyper-plane that separates good marks and bad marks in that space is determined through machine learning process, and corresponds to our decision function. We try a single global function that can be applied to any GP types, and a series of functions in which each function is customized for different GP type; they are then compared and assessed in 10nm technology.

Integrated model reference adaptive control and time-varying angular rate estimation for micro-machined gyroscopes

NASA Astrophysics Data System (ADS)

Tsai, Nan-Chyuan; Sue, Chung-Yang

2010-02-01

Owing to the imposed but undesired accelerations such as quadrature error and cross-axis perturbation, the micro-machined gyroscope would not be unconditionally retained at resonant mode. Once the preset resonance is not sustained, the performance of the micro-gyroscope is accordingly degraded. In this article, a direct model reference adaptive control loop which is integrated with a modified disturbance estimating observer (MDEO) is proposed to guarantee the resonant oscillations at drive mode and counterbalance the undesired disturbance mainly caused by quadrature error and cross-axis perturbation. The parameters of controller are on-line innovated by the dynamic error between the MDEO output and expected response. In addition, Lyapunov stability theory is employed to examine the stability of the closed-loop control system. Finally, the efficacy of numerical evaluation on the exerted time-varying angular rate, which is to be detected and measured by the gyroscope, is verified by intensive simulations.

Micro sculpting technology using DPSSL

NASA Astrophysics Data System (ADS)

Chang, Won-Seok; Shin, Bosung; Kim, Jae-gu; Whang, Kyung-Hyun

2003-11-01

Multiple pulse laser ablation of polymer is performed with DPSS (Diode Pumped Solid State) 3rd harmonic Nd:YVO4 laser (355 nm) in order to fabricate three-dimensional micro components. Here we considered mechanistic aspects of the interaction between UV laser and polymer to obtain optimum process conditions for maskless photomachining using DPSSL. The photo-physical and photochemical parameters such as laser wavelength and optical characteristics of polymers are investigated by experiments to reduce plume effect, which induce the re-deposited debris on the surface of substrate. In this study, LDST (laser direct sculpting technique) are developed to gain various three-dimensional shape with size less than 500 micrometer. Main process sequences are from rapid prototyping technology such as CAD/CAM modeling of products, machining path generation, layer-by-layer machining, and so on. This method can be applied to manufacture the prototype of micro device and the polymer mould for mass production without expensive mask fabrication.

Machinability of nickel based alloys using electrical discharge machining process

NASA Astrophysics Data System (ADS)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

NASA Astrophysics Data System (ADS)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series

NASA Astrophysics Data System (ADS)

Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik

2016-06-01

Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.

The Knife Machine. Module 15.

ERIC Educational Resources Information Center

South Carolina State Dept. of Education, Columbia. Office of Vocational Education.

This module on the knife machine, one in a series dealing with industrial sewing machines, their attachments, and operation, covers one topic: performing special operations on the knife machine (a single needle or multi-needle machine which sews and cuts at the same time). These components are provided: an introduction, directions, an objective,…

Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory

NASA Astrophysics Data System (ADS)

Khanna, Rajesh; Kumar, Anish; Garg, Mohinder Pal; Singh, Ajit; Sharma, Neeraj

2015-12-01

Electric discharge drill machine (EDDM) is a spark erosion process to produce micro-holes in conductive materials. This process is widely used in aerospace, medical, dental and automobile industries. As for the performance evaluation of the electric discharge drilling machine, it is very necessary to study the process parameters of machine tool. In this research paper, a brass rod 2 mm diameter was selected as a tool electrode. The experiments generate output responses such as tool wear rate (TWR). The best parameters such as pulse on-time, pulse off-time and water pressure were studied for best machining characteristics. This investigation presents the use of Taguchi approach for better TWR in drilling of Al-7075. A plan of experiments, based on L27 Taguchi design method, was selected for drilling of material. Analysis of variance (ANOVA) shows the percentage contribution of the control factor in the machining of Al-7075 in EDDM. The optimal combination levels and the significant drilling parameters on TWR were obtained. The optimization results showed that the combination of maximum pulse on-time and minimum pulse off-time gives maximum MRR.

Evaluating the electrical discharge machining (EDM) parameters with using carbon nanotubes

NASA Astrophysics Data System (ADS)

Sari, M. M.; Noordin, M. Y.; Brusa, E.

2012-09-01

Electrical discharge machining (EDM) is one of the most accurate non traditional manufacturing processes available for creating tiny apertures, complex or simple shapes and geometries within parts and assemblies. Performance of the EDM process is usually evaluated in terms of surface roughness, existence of cracks, voids and recast layer on the surface of product, after machining. Unfortunately, the high heat generated on the electrically discharged material during the EDM process decreases the quality of products. Carbon nanotubes display unexpected strength and unique electrical and thermal properties. Multi-wall carbon nanotubes are therefore on purpose added to the dielectric used in the EDM process to improve its performance when machining the AISI H13 tool steel, by means of copper electrodes. Some EDM parameters such as material removal rate, electrode wear rate, surface roughness and recast layer are here first evaluated, then compared to the outcome of EDM performed without using nanotubes mixed to the dielectric. Independent variables investigated are pulse on time, peak current and interval time. Experimental evidences show that EDM process operated by mixing multi-wall carbon nanotubes within the dielectric looks more efficient, particularly if machining parameters are set at low pulse of energy.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry.

PubMed

Silva, Fabrício R; Vidotti, Vanessa G; Cremasco, Fernanda; Dias, Marcelo; Gomi, Edson S; Costa, Vital P

2013-01-01

To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

PubMed

Luo, Gang; Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-08-29

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets. This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. We are currently writing Auto-ML's design document. We intend to finish our study by around the year 2022. Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. ©Gang Luo, Bryan L Stone, Michael D Johnson, Peter Tarczy-Hornoch, Adam B Wilcox, Sean D Mooney, Xiaoming Sheng, Peter J Haug, Flory L Nkoy. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.08.2017.

Patterns of Carbon Nanotubes by Flow-Directed Deposition on Substrates with Architectured Topographies.

PubMed

K Jawed, M; Hadjiconstantinou, N G; Parks, D M; Reis, P M

2018-03-14

We develop and perform continuum mechanics simulations of carbon nanotube (CNT) deployment directed by a combination of surface topography and rarefied gas flow. We employ the discrete elastic rods method to model the deposition of CNT as a slender elastic rod that evolves in time under two external forces, namely, van der Waals (vdW) and aerodynamic drag. Our results confirm that this self-assembly process is analogous to a previously studied macroscopic system, the "elastic sewing machine", where an elastic rod deployed onto a moving substrate forms nonlinear patterns. In the case of CNTs, the complex patterns observed on the substrate, such as coils and serpentines, result from an intricate interplay between van der Waals attraction, rarefied aerodynamics, and elastic bending. We systematically sweep through the multidimensional parameter space to quantify the pattern morphology as a function of the relevant material, flow, and geometric parameters. Our findings are in good agreement with available experimental data. Scaling analysis involving the relevant forces helps rationalize our observations.

Man/Machine Interaction Dynamics And Performance (MMIDAP) capability

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.

Effect of cutting parameters on strain hardening of nickel–titanium shape memory alloy

NASA Astrophysics Data System (ADS)

Wang, Guijie; Liu, Zhanqiang; Ai, Xing; Huang, Weimin; Niu, Jintao

2018-07-01

Nickel–titanium shape memory alloy (SMA) has been widely used as implant materials due to its good biocompatibility, shape memory property and super-elasticity. However, the severe strain hardening is a main challenge due to cutting force and temperature caused by machining. An orthogonal experiment of nickel–titanium SMA with different milling parameters conditions was conducted in this paper. On the one hand, the effect of cutting parameters on work hardening is obtained. It is found that the cutting speed has the most important effect on work hardening. The depth of machining induced layer and the degree of hardening become smaller with the increase of cutting speed when the cutting speed is less than 200 m min‑1 and then get larger with further increase of cutting speed. The relative intensity of diffraction peak increases as the cutting speed increase. In addition, all of the depth of machining induced layer, the degree of hardening and the relative intensity of diffraction peak increase when the feed rate increases. On the other hand, it is found that the depth of machining induced layer is closely related with the degree of hardening and phase transition. The higher the content of austenite in the machined surface is, the higher the degree of hardening will be. The depth of the machining induced layer increases with the degree of hardening increasing.

[The design and experiment of multi-parameter water quality monitoring microsystem based on MOEMS microspectrometer].

PubMed

Wei, Kang-Lin; Wen, Zhi-Yu; Guo, Jian; Chen, Song-Bo

2012-07-01

Aiming at the monitoring and protecting of water resource environment, a multi-parameter water quality monitoring microsystem based on microspectrometer was put forward in the present paper. The microsystem is mainly composed of MOEMS microspectrometer, flow paths system and embedded measuring & controlling system. It has the functions of self-injecting samples and detection regents, automatic constant temperature, self -stirring, self- cleaning and samples' spectrum detection. The principle prototype machine of the microsystem was developed, and its structure principle was introduced in the paper. Through experiment research, it was proved that the principle prototype machine can rapidly detect quite a few water quality parameters and can meet the demands of on-line water quality monitoring, moreover, the principle prototype machine has strong function expansibility.

Uncertainty analysis of an inflow forecasting model: extension of the UNEEC machine learning-based method

NASA Astrophysics Data System (ADS)

Pianosi, Francesca; Lal Shrestha, Durga; Solomatine, Dimitri

2010-05-01

This research presents an extension of UNEEC (Uncertainty Estimation based on Local Errors and Clustering, Shrestha and Solomatine, 2006, 2008 & Solomatine and Shrestha, 2009) method in the direction of explicit inclusion of parameter uncertainty. UNEEC method assumes that there is an optimal model and the residuals of the model can be used to assess the uncertainty of the model prediction. It is assumed that all sources of uncertainty including input, parameter and model structure uncertainty are explicitly manifested in the model residuals. In this research, theses assumptions are relaxed, and the UNEEC method is extended to consider parameter uncertainty as well (abbreviated as UNEEC-P). In UNEEC-P, first we use Monte Carlo (MC) sampling in parameter space to generate N model realizations (each of which is a time series), estimate the prediction quantiles based on the empirical distribution functions of the model residuals considering all the residual realizations, and only then apply the standard UNEEC method that encapsulates the uncertainty of a hydrologic model (expressed by quantiles of the error distribution) in a machine learning model (e.g., ANN). UNEEC-P is applied first to a linear regression model of synthetic data, and then to a real case study of forecasting inflow to lake Lugano in northern Italy. The inflow forecasting model is a stochastic heteroscedastic model (Pianosi and Soncini-Sessa, 2009). The preliminary results show that the UNEEC-P method produces wider uncertainty bounds, which is consistent with the fact that the method considers also parameter uncertainty of the optimal model. In the future UNEEC method will be further extended to consider input and structure uncertainty which will provide more realistic estimation of model predictions.

Numerical Simulation of Earth Pressure on Head Chamber of Shield Machine with FEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Shouju; Kang Chengang; Sun, Wei

2010-05-21

Model parameters of conditioned soils in head chamber of shield machine are determined based on tree-axial compression tests in laboratory. The loads acting on tunneling face are estimated according to static earth pressure principle. Based on Duncan-Chang nonlinear elastic constitutive model, the earth pressures on head chamber of shield machine are simulated in different aperture ratio cases for rotating cutterhead of shield machine. Relationship between pressure transportation factor and aperture ratio of shield machine is proposed by using aggression analysis.

Machine Learning and Inverse Problem in Geodynamics

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques can successfully predict the magnitude of the mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex problems in mantle dynamics by employing deep learning algorithms for estimation of mantle properties such as viscosity, elastic parameters, and thermal and chemical anomalies.

Spin dynamics modeling in the AGS based on a stepwise ray-tracing method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dutheil, Yann

The AGS provides a polarized proton beam to RHIC. The beam is accelerated in the AGS from Gγ= 4.5 to Gγ = 45.5 and the polarization transmission is critical to the RHIC spin program. In the recent years, various systems were implemented to improve the AGS polarization transmission. These upgrades include the double partial snakes configuration and the tune jumps system. However, 100% polarization transmission through the AGS acceleration cycle is not yet reached. The current efficiency of the polarization transmission is estimated to be around 85% in typical running conditions. Understanding the sources of depolarization in the AGS ismore » critical to improve the AGS polarized proton performances. The complexity of beam and spin dynamics, which is in part due to the specialized Siberian snake magnets, drove a strong interest for original methods of simulations. For that, the Zgoubi code, capable of direct particle and spin tracking through field maps, was here used to model the AGS. A model of the AGS using the Zgoubi code was developed and interfaced with the current system through a simple command: the AgsFromSnapRampCmd. Interfacing with the machine control system allows for fast modelization using actual machine parameters. Those developments allowed the model to realistically reproduce the optics of the AGS along the acceleration ramp. Additional developments on the Zgoubi code, as well as on post-processing and pre-processing tools, granted long term multiturn beam tracking capabilities: the tracking of realistic beams along the complete AGS acceleration cycle. Beam multiturn tracking simulations in the AGS, using realistic beam and machine parameters, provided a unique insight into the mechanisms behind the evolution of the beam emittance and polarization during the acceleration cycle. Post-processing softwares were developed to allow the representation of the relevant quantities from the Zgoubi simulations data. The Zgoubi simulations proved particularly useful to better understand the polarization losses through horizontal intrinsic spin resonances The Zgoubi model as well as the tools developed were also used for some direct applications. For instance, some beam experiment simulations allowed an accurate estimation of the expected polarization gains from machine changes. In particular, the simulations that involved involved the tune jumps system provided an accurate estimation of polarization gains and the optimum settings that would improve the performance of the AGS.« less

A system for comparison of boring parameters of mini-HDD machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gunsaulis, F.R.

A system has been developed to accurately evaluate changes in performance of a mini-horizontal directional drilling (HDD) system in the backreaming/pullback portion of a bore as the parameters influencing the backream are changed. Parameters incorporated in the study include spindle rotation rate, rate of pull, fluid flow rate, and backreamer design. The boring system is able to run at variable, operator-determined rates of spindle rotation and pullback speed utilizing electronic feedback controls for regulation. Spindle torque and pullback force are continuously measured and recorded giving an indication of the performance of the unit. A method has also been developed tomore » measure the pull load on the installed service line to determine the effect of the boring parameters on the service line. Variability of soil along the bore path is measured and quantified using a soil sampling system developed for the study. Sample results obtained with the system are included in the report. 2 refs., 5 figs., 2 tabs.« less

Machine characterization based on an abstract high-level language machine

NASA Technical Reports Server (NTRS)

Saavedra-Barrera, Rafael H.; Smith, Alan Jay; Miya, Eugene

1989-01-01

Measurements are presented for a large number of machines ranging from small workstations to supercomputers. The authors combine these measurements into groups of parameters which relate to specific aspects of the machine implementation, and use these groups to provide overall machine characterizations. The authors also define the concept of pershapes, which represent the level of performance of a machine for different types of computation. A metric based on pershapes is introduced that provides a quantitative way of measuring how similar two machines are in terms of their performance distributions. The metric is related to the extent to which pairs of machines have varying relative performance levels depending on which benchmark is used.

Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces.

PubMed

Liu, Jianbo; Khalil, Hassan K; Oweiss, Karim G

2011-10-01

In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.

Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study.

PubMed

Lima, Clodoaldo A M; Coelho, André L V

2011-10-01

We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely, Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). Copyright © 2011 Elsevier B.V. All rights reserved.

SU-E-T-473: A Patient-Specific QC Paradigm Based On Trajectory Log Files and DICOM Plan Files

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeMarco, J; McCloskey, S; Low, D

Purpose: To evaluate a remote QC tool for monitoring treatment machine parameters and treatment workflow. Methods: The Varian TrueBeamTM linear accelerator is a digital machine that records machine axis parameters and MLC leaf positions as a function of delivered monitor unit or control point. This information is saved to a binary trajectory log file for every treatment or imaging field in the patient treatment session. A MATLAB analysis routine was developed to parse the trajectory log files for a given patient, compare the expected versus actual machine and MLC positions as well as perform a cross-comparison with the DICOM-RT planmore » file exported from the treatment planning system. The parsing routine sorts the trajectory log files based on the time and date stamp and generates a sequential report file listing treatment parameters and provides a match relative to the DICOM-RT plan file. Results: The trajectory log parsing-routine was compared against a standard record and verify listing for patients undergoing initial IMRT dosimetry verification and weekly and final chart QC. The complete treatment course was independently verified for 10 patients of varying treatment site and a total of 1267 treatment fields were evaluated including pre-treatment imaging fields where applicable. In the context of IMRT plan verification, eight prostate SBRT plans with 4-arcs per plan were evaluated based on expected versus actual machine axis parameters. The average value for the maximum RMS MLC error was 0.067±0.001mm and 0.066±0.002mm for leaf bank A and B respectively. Conclusion: A real-time QC analysis program was tested using trajectory log files and DICOM-RT plan files. The parsing routine is efficient and able to evaluate all relevant machine axis parameters during a patient treatment course including MLC leaf positions and table positions at time of image acquisition and during treatment.« less

Parameter monitoring compensation system and method

DOEpatents

Barkman, William E.; Babelay, Edwin F.; DeMint, Paul D.; Hebble, Thomas L.; Igou, Richard E.; Williams, Richard R.; Klages, Edward J.; Rasnick, William H.

1995-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along preprogrammed path during a machining operation utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer.

Reducing the uncertainty in robotic machining by modal analysis

NASA Astrophysics Data System (ADS)

Alberdi, Iñigo; Pelegay, Jose Angel; Arrazola, Pedro Jose; Ørskov, Klaus Bonde

2017-10-01

The use of industrial robots for machining could lead to high cost and energy savings for the manufacturing industry. Machining robots offer several advantages respect to CNC machines such as flexibility, wide working space, adaptability and relatively low cost. However, there are some drawbacks that are preventing a widespread adoption of robotic solutions namely lower stiffness, vibration/chatter problems and lower accuracy and repeatability. Normally due to these issues conservative cutting parameters are chosen, resulting in a low material removal rate (MRR). In this article, an example of a modal analysis of a robot is presented. For that purpose the Tap-testing technology is introduced, which aims at maximizing productivity, reducing the uncertainty in the selection of cutting parameters and offering a stable process free from chatter vibrations.

Effect of hole geometry and Electric-Discharge Machining (EDM) on airflow rates through small diameter holes in turbine blade material

NASA Technical Reports Server (NTRS)

Hippensteele, S. A.; Cochran, R. P.

1980-01-01

The effects of two design parameters, electrode diameter and hole angle, and two machine parameters, electrode current and current-on time, on air flow rates through small-diameter (0.257 to 0.462 mm) electric-discharge-machined holes were measured. The holes were machined individually in rows of 14 each through 1.6 mm thick IN-100 strips. The data showed linear increase in air flow rate with increases in electrode cross sectional area and current-on time and little change with changes in hole angle and electrode current. The average flow-rate deviation (from the mean flow rate for a given row) decreased linearly with electrode diameter and increased with hole angle. Burn time and finished hole diameter were also measured.

Exploring the influence of constitutive models and associated parameters for the orthogonal machining of Ti6Al4V

NASA Astrophysics Data System (ADS)

Pervaiz, S.; Anwar, S.; Kannan, S.; Almarfadi, A.

2018-04-01

Ti6Al4V is known as difficult-to-cut material due to its inherent properties such as high hot hardness, low thermal conductivity and high chemical reactivity. Though, Ti6Al4V is utilized by industrial sectors such as aeronautics, energy generation, petrochemical and bio-medical etc. For the metal cutting community, competent and cost-effective machining of Ti6Al4V is a challenging task. To optimize cost and machining performance for the machining of Ti6Al4V, finite element based cutting simulation can be a very useful tool. The aim of this paper is to develop a finite element machining model for the simulation of Ti6Al4V machining process. The study incorporates material constitutive models namely Power Law (PL) and Johnson – Cook (JC) material models to mimic the mechanical behaviour of Ti6Al4V. The study investigates cutting temperatures, cutting forces, stresses, and plastic strains with respect to different PL and JC material models with associated parameters. In addition, the numerical study also integrates different cutting tool rake angles in the machining simulations. The simulated results will be beneficial to draw conclusions for improving the overall machining performance of Ti6Al4V.

Effect of magnetic polarity on surface roughness during magnetic field assisted EDM of tool steel

NASA Astrophysics Data System (ADS)

Efendee, A. M.; Saifuldin, M.; Gebremariam, MA; Azhari, A.

2018-04-01

Electrical discharge machining (EDM) is one of the non-traditional machining techniques where the process offers wide range of parameters manipulation and machining applications. However, surface roughness, material removal rate, electrode wear and operation costs were among the topmost issue within this technique. Alteration of magnetic device around machining area offers exciting output to be investigated and the effects of magnetic polarity on EDM remain unacquainted. The aim of this research is to investigate the effect of magnetic polarity on surface roughness during magnetic field assisted electrical discharge machining (MFAEDM) on tool steel material (AISI 420 mod.) using graphite electrode. A Magnet with a force of 18 Tesla was applied to the EDM process at selected parameters. The sparks under magnetic field assisted EDM produced better surface finish than the normal conventional EDM process. At the presence of high magnetic field, the spark produced was squeezed and discharge craters generated on the machined surface was tiny and shallow. Correct magnetic polarity combination of MFAEDM process is highly useful to attain a high efficiency machining and improved quality of surface finish to meet the demand of modern industrial applications.

Axial gap rotating electrical machine

DOEpatents

None

2016-02-23

Direct drive rotating electrical machines with axial air gaps are disclosed. In these machines, a rotor ring and stator ring define an axial air gap between them. Sets of gap-maintaining rolling supports bear between the rotor ring and the stator ring at their peripheries to maintain the axial air gap. Also disclosed are wind turbines using these generators, and structures and methods for mounting direct drive rotating electrical generators to the hubs of wind turbines. In particular, the rotor ring of the generator may be carried directly by the hub of a wind turbine to rotate relative to a shaft without being mounted directly to the shaft.

Tool geometry and damage mechanisms influencing CNC turning efficiency of Ti6Al4V

NASA Astrophysics Data System (ADS)

Suresh, Sangeeth; Hamid, Darulihsan Abdul; Yazid, M. Z. A.; Nasuha, Nurdiyanah; Ain, Siti Nurul

2017-12-01

Ti6Al4V or Grade 5 titanium alloy is widely used in the aerospace, medical, automotive and fabrication industries, due to its distinctive combination of mechanical and physical properties. Ti6Al4V has always been perverse during its machining, strangely due to the same mix of properties mentioned earlier. Ti6Al4V machining has resulted in shorter cutting tool life which has led to objectionable surface integrity and rapid failure of the parts machined. However, the proven functional relevance of this material has prompted extensive research in the optimization of machine parameters and cutting tool characteristics. Cutting tool geometry plays a vital role in ensuring dimensional and geometric accuracy in machined parts. In this study, an experimental investigation is actualized to optimize the nose radius and relief angles of the cutting tools and their interaction to different levels of machining parameters. Low elastic modulus and thermal conductivity of Ti6Al4V contribute to the rapid tool damage. The impact of these properties over the tool tips damage is studied. An experimental design approach is utilized in the CNC turning process of Ti6Al4V to statistically analyze and propose optimum levels of input parameters to lengthen the tool life and enhance surface characteristics of the machined parts. A greater tool nose radius with a straight flank, combined with low feed rates have resulted in a desirable surface integrity. The presence of relief angle has proven to aggravate tool damage and also dimensional instability in the CNC turning of Ti6Al4V.

Supervised Learning of Two-Layer Perceptron under the Existence of External Noise — Learning Curve of Boolean Functions of Two Variables in Tree-Like Architecture —

NASA Astrophysics Data System (ADS)

Uezu, Tatsuya; Kiyokawa, Shuji

2016-06-01

We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α C)bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.

Design and experimental validation for direct-drive fault-tolerant permanent-magnet vernier machines.

PubMed

Liu, Guohai; Yang, Junqin; Chen, Ming; Chen, Qian

2014-01-01

A fault-tolerant permanent-magnet vernier (FT-PMV) machine is designed for direct-drive applications, incorporating the merits of high torque density and high reliability. Based on the so-called magnetic gearing effect, PMV machines have the ability of high torque density by introducing the flux-modulation poles (FMPs). This paper investigates the fault-tolerant characteristic of PMV machines and provides a design method, which is able to not only meet the fault-tolerant requirements but also keep the ability of high torque density. The operation principle of the proposed machine has been analyzed. The design process and optimization are presented specifically, such as the combination of slots and poles, the winding distribution, and the dimensions of PMs and teeth. By using the time-stepping finite element method (TS-FEM), the machine performances are evaluated. Finally, the FT-PMV machine is manufactured, and the experimental results are presented to validate the theoretical analysis.

Application of TRIZ approach to machine vibration condition monitoring problems

NASA Astrophysics Data System (ADS)

Cempel, Czesław

2013-12-01

Up to now machine condition monitoring has not been seriously approached by TRIZ1TRIZ= Russian acronym for Inventive Problem Solving System, created by G. Altshuller ca 50 years ago. users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR) of our innovation problem. As a next we need a set of parameters to describe the problems of system condition monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.

Investigation the gas film in micro scale induced error on the performance of the aerostatic spindle in ultra-precision machining

NASA Astrophysics Data System (ADS)

Chen, Dongju; Huo, Chen; Cui, Xianxian; Pan, Ri; Fan, Jinwei; An, Chenhui

2018-05-01

The objective of this work is to study the influence of error induced by gas film in micro-scale on the static and dynamic behavior of a shaft supported by the aerostatic bearings. The static and dynamic balance models of the aerostatic bearing are presented by the calculated stiffness and damping in micro scale. The static simulation shows that the deformation of aerostatic spindle system in micro scale is decreased. For the dynamic behavior, both the stiffness and damping in axial and radial directions are increased in micro scale. The experiments of the stiffness and rotation error of the spindle show that the deflection of the shaft resulting from the calculating parameters in the micro scale is very close to the deviation of the spindle system. The frequency information in transient analysis is similar to the actual test, and they are also higher than the results from the traditional case without considering micro factor. Therefore, it can be concluded that the value considering micro factor is closer to the actual work case of the aerostatic spindle system. These can provide theoretical basis for the design and machining process of machine tools.

Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders.

PubMed

Viejo, Guillaume; Cortier, Thomas; Peyrache, Adrien

2018-03-01

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains.

Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders

PubMed Central

Cortier, Thomas; Peyrache, Adrien

2018-01-01

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains. PMID:29565979

Technical Report on Occupations in Numerically Controlled Metal-Cutting Machining.

ERIC Educational Resources Information Center

Manpower Administration (DOL), Washington, DC. U.S. Employment Service.

At the present time, only 5 percent of the short-run metal-cutting machining in the United States is done by numerically controlled machined tools, but within the next decade it is expected to increase by 50 percent. Numerically controlled machines use taped data which is changed into instructions and directs the machine to do certain steps…

Direct fired absorption machine flue gas recuperator

DOEpatents

Reimann, Robert C.; Root, Richard A.

1985-01-01

A recuperator which recovers heat from a gas, generally the combustion gas of a direct-fired generator of an absorption machine. The recuperator includes a housing with liquid flowing therethrough, the liquid being in direct contact with the combustion gas for increasing the effectiveness of the heat transfer between the gas and the liquid.

Rapid performance modeling and parameter regression of geodynamic models

NASA Astrophysics Data System (ADS)

Brown, J.; Duplyakin, D.

2016-12-01

Geodynamic models run in a parallel environment have many parameters with complicated effects on performance and scientifically-relevant functionals. Manually choosing an efficient machine configuration and mapping out the parameter space requires a great deal of expert knowledge and time-consuming experiments. We propose an active learning technique based on Gaussion Process Regression to automatically select experiments to map out the performance landscape with respect to scientific and machine parameters. The resulting performance model is then used to select optimal experiments for improving the accuracy of a reduced order model per unit of computational cost. We present the framework and evaluate its quality and capability using popular lithospheric dynamics models.

CNC Machining Of The Complex Copper Electrodes

NASA Astrophysics Data System (ADS)

Popan, Ioan Alexandru; Balc, Nicolae; Popan, Alina

2015-07-01

This paper presents the machining process of the complex copper electrodes. Machining of the complex shapes in copper is difficult because this material is soft and sticky. This research presents the main steps for processing those copper electrodes at a high dimensional accuracy and a good surface quality. Special tooling solutions are required for this machining process and optimal process parameters have been found for the accurate CNC equipment, using smart CAD/CAM software.

Machine-learned and codified synthesis parameters of oxide materials

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa

2017-09-01

Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.

The Simpsons program 6-D phase space tracking with acceleration

NASA Astrophysics Data System (ADS)

Machida, S.

1993-12-01

A particle tracking code, Simpsons, in 6-D phase space including energy ramping has been developed to model proton synchrotrons and storage rings. We take time as the independent variable to change machine parameters and diagnose beam quality in a quite similar way as real machines, unlike existing tracking codes for synchrotrons which advance a particle element by element. Arbitrary energy ramping and rf voltage curves as a function of time are read as an input file for defining a machine cycle. The code is used to study beam dynamics with time dependent parameters. Some of the examples from simulations of the Superconducting Super Collider (SSC) boosters are shown.

Design and Development of Maize Dehusker cum Sheller: A Technology for Northern Transition Zone of Karnataka, India

NASA Astrophysics Data System (ADS)

Chilur, Rudragouda; Kumar, Sushilendra

2018-06-01

The Maize ( Zea mays L.) crop is one of the most important cereal in agricultural production systems of Northern Transition Zone (Hyderabad-Karnataka region) in India. These Hyderabad Karnataka farmers (small-medium) are lack of economic technologies with maize dehusking and shelling, which fulfils the two major needs as crops and as livestock in farming. The portable medium size (600 kg/h capacity) electric motor (2.23 kW) operated Maize Dehusker cum Sheller (MDS) was designed to resolve the issue by considering engineering properties of maize. The developed trapezium shaped MDS machine having overall dimensions (length × (top and bottom) × height) of 1200 × (500 and 610) × 810 mm. The selected operational parameters viz, cylinder peripheral speed (7.1 m/s), concave clearance (25 mm) and feed rate (600 kg/h) were studied for machine-performance and seed-quality parameters. The performance of machine under these parameters showed the dehusking efficiency of 99.56%, shelling efficiency of 98.01%, cleaning efficiency of 99.11%, total loss of 3.63% machine capacity of 527.11 kg/kW-h and germination percentage of 98.93%. Overall machine performance was found satisfactory for maize dehusking cum shelling operation as well as to produce the maize grains for seeding purpose.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Design and Development of Maize Dehusker cum Sheller: A Technology for Northern Transition Zone of Karnataka, India

NASA Astrophysics Data System (ADS)

Chilur, Rudragouda; Kumar, Sushilendra

2018-02-01

The Maize (Zea mays L.) crop is one of the most important cereal in agricultural production systems of Northern Transition Zone (Hyderabad-Karnataka region) in India. These Hyderabad Karnataka farmers (small-medium) are lack of economic technologies with maize dehusking and shelling, which fulfils the two major needs as crops and as livestock in farming. The portable medium size (600 kg/h capacity) electric motor (2.23 kW) operated Maize Dehusker cum Sheller (MDS) was designed to resolve the issue by considering engineering properties of maize. The developed trapezium shaped MDS machine having overall dimensions (length × (top and bottom) × height) of 1200 × (500 and 610) × 810 mm. The selected operational parameters viz, cylinder peripheral speed (7.1 m/s), concave clearance (25 mm) and feed rate (600 kg/h) were studied for machine-performance and seed-quality parameters. The performance of machine under these parameters showed the dehusking efficiency of 99.56%, shelling efficiency of 98.01%, cleaning efficiency of 99.11%, total loss of 3.63% machine capacity of 527.11 kg/kW-h and germination percentage of 98.93%. Overall machine performance was found satisfactory for maize dehusking cum shelling operation as well as to produce the maize grains for seeding purpose.

Effectiveness of Direct Safety Regulations on Manufacturers and Users of Industrial Machines: Its Implications on Industrial Safety Policies in Republic of Korea.

PubMed

Choi, Gi Heung

2017-03-01

Despite considerable efforts made in recent years, the industrial accident rate and the fatality rate in the Republic of Korea are much higher than those in most developed countries in Europe and North America. Industrial safety policies and safety regulations are also known to be ineffective and inefficient in some cases. This study focuses on the quantitative evaluation of the effectiveness of direct safety regulations such as safety certification, self-declaration of conformity, and safety inspection of industrial machines in the Republic of Korea. Implications on safety policies to restructure the industrial safety system associated with industrial machines are also explored. Analysis of causes in industrial accidents associated with industrial machines confirms that technical causes need to be resolved to reduce both the frequency and the severity of such industrial accidents. Statistical analysis also confirms that the indirect effects of safety device regulation on users are limited for a variety of reasons. Safety device regulation needs to be shifted to complement safety certification and self-declaration of conformity for more balanced direct regulations on manufacturers and users. An example of cost-benefit analysis on conveyor justifies such a transition. Industrial safety policies and regulations associated with industrial machines must be directed towards eliminating the sources of danger at the stage of danger creation, thereby securing the safe industrial machines. Safety inspection further secures the safety of workers at the stage of danger use. The overall balance between such safety regulations is achieved by proper distribution of industrial machines subject to such regulations and the intensity of each regulation. Rearrangement of industrial machines subject to safety certification and self-declaration of conformity to include more movable industrial machines and other industrial machines with a high level of danger is also suggested.

Gesture-controlled interfaces for self-service machines and other applications

NASA Technical Reports Server (NTRS)

Cohen, Charles J. (Inventor); Jacobus, Charles J. (Inventor); Paul, George (Inventor); Beach, Glenn (Inventor); Foulk, Gene (Inventor); Obermark, Jay (Inventor); Cavell, Brook (Inventor)

2004-01-01

A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.

Study on the Optimization and Process Modeling of the Rotary Ultrasonic Machining of Zerodur Glass-Ceramic

NASA Astrophysics Data System (ADS)

Pitts, James Daniel

Rotary ultrasonic machining (RUM), a hybrid process combining ultrasonic machining and diamond grinding, was created to increase material removal rates for the fabrication of hard and brittle workpieces. The objective of this research was to experimentally derive empirical equations for the prediction of multiple machined surface roughness parameters for helically pocketed rotary ultrasonic machined Zerodur glass-ceramic workpieces by means of a systematic statistical experimental approach. A Taguchi parametric screening design of experiments was employed to systematically determine the RUM process parameters with the largest effect on mean surface roughness. Next empirically determined equations for the seven common surface quality metrics were developed via Box-Behnken surface response experimental trials. Validation trials were conducted resulting in predicted and experimental surface roughness in varying levels of agreement. The reductions in cutting force and tool wear associated with RUM, reported by previous researchers, was experimentally verified to also extended to helical pocketing of Zerodur glass-ceramic.

Developing Lathing Parameters for PBX 9501

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodrum, Randall Brock

This thesis presents the work performed on lathing PBX 9501 to gather and analyze cutting force and temperature data during the machining process. This data will be used to decrease federal-regulation-constrained machining time of the high explosive PBX 9501. The effects of machining parameters depth of cut, surface feet per minute, and inches per revolution on cutting force and cutting interface were evaluated. Cutting tools of tip radius 0.005 -inches and 0.05 -inches were tested to determine what effect the tool shape had on the machining process as well. A consistently repeatable relationship of temperature to changing depth of cutmore » and surface feet per minute is found, while only a weak dependence was found to changing inches per revolution. Results also show the relation of cutting force to depth of cut and inches per revolution, while weak dependence on SFM is found. Conclusions suggest rapid, shallow cuts optimize machining time for a billet of PBX 9501, while minimizing temperature increase and cutting force.« less

From "Gut Feeling" to Objectivity: Machine Preservation of the Liver as a Tool to Assess Organ Viability.

PubMed

Watson, Christopher J E; Jochmans, Ina

2018-01-01

The purpose of this review was to summarise how machine perfusion could contribute to viability assessment of donor livers. In both hypothermic and normothermic machine perfusion, perfusate transaminase measurement has allowed pretransplant assessment of hepatocellular damage. Hypothermic perfusion permits transplantation of marginal grafts but as yet has not permitted formal viability assessment. Livers undergoing normothermic perfusion have been investigated using parameters similar to those used to evaluate the liver in vivo. Lactate clearance, glucose evolution and pH regulation during normothermic perfusion seem promising measures of viability. In addition, bile chemistry might inform on cholangiocyte viability and the likelihood of post-transplant cholangiopathy. While the use of machine perfusion technology has the potential to reduce and even remove uncertainty regarding liver graft viability, analysis of large datasets, such as those derived from large multicenter trials of machine perfusion, are needed to provide sufficient information to enable viability parameters to be defined and validated .

Machinability of Green Powder Metallurgy Components: Part I. Characterization of the Influence of Tool Wear

NASA Astrophysics Data System (ADS)

Robert-Perron, Etienne; Blais, Carl; Pelletier, Sylvain; Thomas, Yannig

2007-06-01

The green machining process is an interesting approach for solving the mediocre machining behavior of high-performance powder metallurgy (PM) steels. This process appears as a promising method for extending tool life and reducing machining costs. Recent improvements in binder/lubricant technologies have led to high green strength systems that enable green machining. So far, tool wear has been considered negligible when characterizing the machinability of green PM specimens. This inaccurate assumption may lead to the selection of suboptimum cutting conditions. The first part of this study involves the optimization of the machining parameters to minimize the effects of tool wear on the machinability in turning of green PM components. The second part of our work compares the sintered mechanical properties of components machined in green state with other machined after sintering.

Highly Productive Tools For Turning And Milling

NASA Astrophysics Data System (ADS)

Vasilko, Karol

2015-12-01

Beside cutting speed, shift is another important parameter of machining. Its considerable influence is shown mainly in the workpiece machined surface microgeometry. In practice, mainly its combination with the radius of cutting tool tip rounding is used. Options to further increase machining productivity and machined surface quality are hidden in this approach. The paper presents variations of the design of productive cutting tools for lathe work and milling on the base of the use of the laws of the relationship among the highest reached uneveness of machined surface, tool tip radius and shift.

A novel pulsed gas metal arc welding system with direct droplet transfer close-loop control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Q.; Li, P.; Zhang, L.

1994-12-31

In pulsed gas metal arc welding (GMAW), a predominant parameter that has to be monitored and controlled in real time for maintaining process stability and ensuring weld quality, is droplet transfer. Based on the close correlation between droplet transfer and arc light radiant flux in GMAW of steel and aluminum, a direct closed-loop droplet transfer control system for pulsed GMAW with arc light sensor has been developed. By sensing the droplet transfer directly via the arc light signal, a pulsed GMAW process with real and exact one-pulse, one-droplet transfer has been achieved. The novel pulsed GMAW machine consists of threemore » parts: a sensing system, a controlling system, and a welding power system. The software used in this control system is capable of data sampling and processing, parameter matching, optimum parameter restoring, and resetting. A novel arc light sensing system has been developed. The sensor is small enough to be clamped to a semiautomatic welding torch. Based on thissensingn system, a closed-loop droplet transfer control system of GMAW of steel and aluminum has been built and a commercial prototype has been made. The system is capable of keeping one-pulse, one-droplet transfer against external interferences. The welding process with this control system has been proved to be stable, quiet, with no spatter, and provide good weld formation.« less

Parameter monitoring compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F.; DeMint, P.D.; Hebble, T.L.; Igou, R.E.; Williams, R.R.; Klages, E.J.; Rasnick, W.H.

1995-02-07

A compensation system is described for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation. It utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer. 7 figs.

Experimental investigation of various surface integrity aspects in hard turning of AISI 4340 alloy steel with coated and uncoated cermet

NASA Astrophysics Data System (ADS)

Das, Anshuman; Patel, S. K.; Sateesh Kumar, Ch.; Biswal, B. B.

2018-03-01

The newer technological developments are exerting immense pressure on domain of production. These fabrication industries are busy finding solutions to reduce the costs of cutting materials, enhance the machined parts quality and testing different materials, which can be made versatile for cutting materials, which are difficult for machining. High-speed machining has been the domain of paramount importance for mechanical engineering. In this study, the variation of surface integrity parameters of hardened AISI 4340 alloy steel was analyzed. The surface integrity parameters like surface roughness, micro hardness, machined surface morphology and white layer of hardened AISI 4340 alloy steel were compared using coated and uncoated cermet inserts under dry cutting condition. From the results, it was deduced that coated insert outperformed uncoated one in terms of different surface integrity characteristics.

Biomachining - A new approach for micromachining of metals

NASA Astrophysics Data System (ADS)

Vigneshwaran, S. C. Sakthi; Ramakrishnan, R.; Arun Prakash, C.; Sashank, C.

2018-04-01

Machining is the process of removal of material from workpiece. Machining can be done by physical, chemical or biological methods. Though physical and chemical methods have been widely used in machining process, they have their own disadvantages such as development of heat affected zone and usage of hazardous chemicals. Biomachining is the machining process in which bacteria is used to remove material from the metal parts. Chemolithotrophic bacteria such as Acidothiobacillus ferroxidans has been used in biomachining of metals like copper, iron etc. These bacteria are used because of their property of catalyzing the oxidation of inorganic substances. Biomachining is a suitable process for micromachining of metals. This paper reviews the biomachining process and various mechanisms involved in biomachining. This paper also briefs about various parameters/factors to be considered in biomachining and also the effect of those parameters on metal removal rate.

High speed machining of space shuttle external tank liquid hydrogen barrel panel

NASA Technical Reports Server (NTRS)

Hankins, J. D.

1983-01-01

Actual and projected optimum High Speed Machining data for producing shuttle external tank liquid hydrogen barrel panels of aluminum alloy 2219-T87 are reported. The data included various machining parameters; e.g., spindle speeds, cutting speed, table feed, chip load, metal removal rate, horsepower, cutting efficiency, cutter wear (lack of) and chip removal methods.

High speed machining of space shuttle external tank liquid hydrogen barrel panel

NASA Astrophysics Data System (ADS)

Hankins, J. D.

1983-11-01

Actual and projected optimum High Speed Machining data for producing shuttle external tank liquid hydrogen barrel panels of aluminum alloy 2219-T87 are reported. The data included various machining parameters; e.g., spindle speeds, cutting speed, table feed, chip load, metal removal rate, horsepower, cutting efficiency, cutter wear (lack of) and chip removal methods.

Agreement Between Institutional Measurements and Treatment Planning System Calculations for Basic Dosimetric Parameters as Measured by the Imaging and Radiation Oncology Core-Houston

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerns, James R.; Followill, David S.; Imaging and Radiation Oncology Core-Houston, The University of Texas Health Science Center-Houston, Houston, Texas

Purpose: To compare radiation machine measurement data collected by the Imaging and Radiation Oncology Core at Houston (IROC-H) with institutional treatment planning system (TPS) values, to identify parameters with large differences in agreement; the findings will help institutions focus their efforts to improve the accuracy of their TPS models. Methods and Materials: Between 2000 and 2014, IROC-H visited more than 250 institutions and conducted independent measurements of machine dosimetric data points, including percentage depth dose, output factors, off-axis factors, multileaf collimator small fields, and wedge data. We compared these data with the institutional TPS values for the same points bymore » energy, class, and parameter to identify differences and similarities using criteria involving both the medians and standard deviations for Varian linear accelerators. Distributions of differences between machine measurements and institutional TPS values were generated for basic dosimetric parameters. Results: On average, intensity modulated radiation therapy–style and stereotactic body radiation therapy–style output factors and upper physical wedge output factors were the most problematic. Percentage depth dose, jaw output factors, and enhanced dynamic wedge output factors agreed best between the IROC-H measurements and the TPS values. Although small differences were shown between 2 common TPS systems, neither was superior to the other. Parameter agreement was constant over time from 2000 to 2014. Conclusions: Differences in basic dosimetric parameters between machine measurements and TPS values vary widely depending on the parameter, although agreement does not seem to vary by TPS and has not changed over time. Intensity modulated radiation therapy–style output factors, stereotactic body radiation therapy–style output factors, and upper physical wedge output factors had the largest disagreement and should be carefully modeled to ensure accuracy.« less

A new optimization tool path planning for 3-axis end milling of free-form surfaces based on efficient machining intervals

NASA Astrophysics Data System (ADS)

Vu, Duy-Duc; Monies, Frédéric; Rubio, Walter

2018-05-01

A large number of studies, based on 3-axis end milling of free-form surfaces, seek to optimize tool path planning. Approaches try to optimize the machining time by reducing the total tool path length while respecting the criterion of the maximum scallop height. Theoretically, the tool path trajectories that remove the most material follow the directions in which the machined width is the largest. The free-form surface is often considered as a single machining area. Therefore, the optimization on the entire surface is limited. Indeed, it is difficult to define tool trajectories with optimal feed directions which generate largest machined widths. Another limiting point of previous approaches for effectively reduce machining time is the inadequate choice of the tool. Researchers use generally a spherical tool on the entire surface. However, the gains proposed by these different methods developed with these tools lead to relatively small time savings. Therefore, this study proposes a new method, using toroidal milling tools, for generating toolpaths in different regions on the machining surface. The surface is divided into several regions based on machining intervals. These intervals ensure that the effective radius of the tool, at each cutter-contact points on the surface, is always greater than the radius of the tool in an optimized feed direction. A parallel plane strategy is then used on the sub-surfaces with an optimal specific feed direction for each sub-surface. This method allows one to mill the entire surface with efficiency greater than with the use of a spherical tool. The proposed method is calculated and modeled using Maple software to find optimal regions and feed directions in each region. This new method is tested on a free-form surface. A comparison is made with a spherical cutter to show the significant gains obtained with a toroidal milling cutter. Comparisons with CAM software and experimental validations are also done. The results show the efficiency of the method.

Research on axisymmetric aspheric surface numerical design and manufacturing technology

NASA Astrophysics Data System (ADS)

Wang, Zhen-zhong; Guo, Yin-biao; Lin, Zheng

2006-02-01

The key technology for aspheric machining offers exact machining path and machining aspheric lens with high accuracy and efficiency, in spite of the development of traditional manual manufacturing into nowadays numerical control (NC) machining. This paper presents a mathematical model between virtual cone and aspheric surface equations, and discusses the technology of uniform wear of grinding wheel and error compensation in aspheric machining. Finally, a software system for high precision aspheric surface manufacturing is designed and realized, based on the mentioned above. This software system can work out grinding wheel path according to input parameters and generate machining NC programs of aspheric surfaces.

Formability behavior studies on CP-Al sheets processed through the helical tool path of incremental forming process

NASA Astrophysics Data System (ADS)

Markanday, H.; Nagarajan, D.

2018-02-01

Incremental sheet forming (ISF) is a novel die-less sheet metal forming process, which can produce components directly from the CAD geometry using a CNC milling machine at less production time and cost. The formability of the sheet material used is greatly affected by the process parameters involved and tool path adopted, and the present study is aimed to investigate the influence of different process parameter values using the helical tool path strategy on the formability of a commercial pure Al and to achieve maximum formability in the material. ISF experiments for producing an 80 mm diameter axisymmetric dome were carried out on 2 mm thickness commercially pure Al sheets for different tool speeds and feed rates in a CNC milling machine with a 10 mm hemispherical forming tool. The obtained parts were analyzed for springback, amount of thinning and maximum forming depth. The results showed that when the tool speed was increased by keeping the feed rate constant, the forming depth and thinning were also increased. On contrary, when the feed rate was increased by keeping the tool speed constant, the forming depth and thinning were decreased. Springback was found to be higher when the feed rate was increased rather than the tool speed was increased.

Chip breaking system for automated machine tool

DOEpatents

Arehart, Theodore A.; Carey, Donald O.

1987-01-01

The invention is a rotary selectively directional valve assembly for use in an automated turret lathe for directing a stream of high pressure liquid machining coolant to the interface of a machine tool and workpiece for breaking up ribbon-shaped chips during the formation thereof so as to inhibit scratching or other marring of the machined surfaces by these ribbon-shaped chips. The valve assembly is provided by a manifold arrangement having a plurality of circumferentially spaced apart ports each coupled to a machine tool. The manifold is rotatable with the turret when the turret is positioned for alignment of a machine tool in a machining relationship with the workpiece. The manifold is connected to a non-rotational header having a single passageway therethrough which conveys the high pressure coolant to only the port in the manifold which is in registry with the tool disposed in a working relationship with the workpiece. To position the machine tools the turret is rotated and one of the tools is placed in a material-removing relationship of the workpiece. The passageway in the header and one of the ports in the manifold arrangement are then automatically aligned to supply the machining coolant to the machine tool workpiece interface for breaking up of the chips as well as cooling the tool and workpiece during the machining operation.

Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

PubMed Central

Townsend, K A; Wollstein, G; Danks, D; Sung, K R; Ishikawa, H; Kagemann, L; Gabriele, M L; Schuman, J S

2010-01-01

Aims To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes. Methods Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created with backward elimination. Seven types of classifiers, including Support Vector Machines with radial basis (SVM-radial), and Recursive Partitioning and Regression Trees (RPART), were trained on the parameters. The area under the ROC curve (AUC) was calculated for classifiers, individual parameters and HRT3 glaucoma probability scores (GPS). Classifier AUCs and leave-one-out accuracy were compared with the highest individual parameter and GPS AUCs and accuracies. Results The highest AUC and accuracy for an individual parameter were 0.848 and 0.79, for vertical cup/disc ratio (vC/D). For GPS, global GPS performed best with AUC 0.829 and accuracy 0.78. SVM-radial with all parameters showed significant improvement over global GPS and vC/ D with AUC 0.916 and accuracy 0.85. RPART with all parameters provided significant improvement over global GPS with AUC 0.899 and significant improvement over global GPS and vC/D with accuracy 0.875. Conclusions Machine learning classifiers of HRT3 data provide significant enhancement over current methods for detection of glaucoma. PMID:18523087

Development of automated control system for wood drying

NASA Astrophysics Data System (ADS)

Sereda, T. G.; Kostarev, S. N.

2018-05-01

The article considers the parameters of convective wood drying which allows changing the characteristics of the air that performs drying at different stages: humidity, temperature, speed and direction of air movement. Despite the prevalence of this type of drying equipment, the main drawbacks of it are: the high temperature and humidity, negatively affecting the working conditions of maintenance personnel when they enter the drying chambers. It makes the automation of wood drying process necessary. The synthesis of a finite state of a machine control of wood drying process is implemented on a programmable logic device Omron.

Computation of the Distribution of the Fiber-Matrix Interface Cracks in the Edge Trimming of CFRP

NASA Astrophysics Data System (ADS)

Wang, Fu-ji; Zhang, Bo-yu; Ma, Jian-wei; Bi, Guang-jian; Hu, Hai-bo

2018-04-01

Edge trimming is commonly used to bring the CFRP components to right dimension and shape in aerospace industries. However, various forms of undesirable machining damage occur frequently which will significantly decrease the material performance of CFRP. The damage is difficult to predict and control due to the complicated changing laws, causing unsatisfactory machining quality of CFRP components. Since the most of damage has the same essence: the fiber-matrix interface cracks, this study aims to calculate the distribution of them in edge trimming of CFRP, thereby to obtain the effects of the machining parameters, which could be helpful to guide the optimal selection of the machining parameters in engineering. Through the orthogonal cutting experiments, the quantitative relation between the fiber-matrix interface crack depth and the fiber cutting angle, cutting depth as well as cutting speed is established. According to the analysis on material removal process on any location of the workpiece in edge trimming, the instantaneous cutting parameters are calculated, and the formation process of the fiber-matrix interface crack is revealed. Finally, the computational method for the fiber-matrix interface cracks in edge trimming of CFRP is proposed. Upon the computational results, it is found that the fiber orientations of CFRP workpieces is the most significant factor on the fiber-matrix interface cracks, which can not only change the depth of them from micrometers to millimeters, but control the distribution image of them. Other machining parameters, only influence the fiber-matrix interface cracks depth but have little effect on the distribution image.

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

PubMed

Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

2014-01-01

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Temperature based Restricted Boltzmann Machines

NASA Astrophysics Data System (ADS)

Li, Guoqi; Deng, Lei; Xu, Yi; Wen, Changyun; Wang, Wei; Pei, Jing; Shi, Luping

2016-01-01

Restricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key factor of the Boltzmann distribution that RBMs originate from. However, none of existing schemes have considered the impact of temperature in the graphical model of DBNs. In this work, we propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers. We theoretically prove that the effect of temperature can be adjusted by setting the parameter of the sharpness of the logistic function in the proposed TRBMs. The performance of RBMs can be improved by adjusting the temperature parameter of TRBMs. This work provides a comprehensive insights into the deep belief networks and deep learning architectures from a physical point of view.

Design and Experimental Validation for Direct-Drive Fault-Tolerant Permanent-Magnet Vernier Machines

PubMed Central

Liu, Guohai; Yang, Junqin; Chen, Ming; Chen, Qian

2014-01-01

A fault-tolerant permanent-magnet vernier (FT-PMV) machine is designed for direct-drive applications, incorporating the merits of high torque density and high reliability. Based on the so-called magnetic gearing effect, PMV machines have the ability of high torque density by introducing the flux-modulation poles (FMPs). This paper investigates the fault-tolerant characteristic of PMV machines and provides a design method, which is able to not only meet the fault-tolerant requirements but also keep the ability of high torque density. The operation principle of the proposed machine has been analyzed. The design process and optimization are presented specifically, such as the combination of slots and poles, the winding distribution, and the dimensions of PMs and teeth. By using the time-stepping finite element method (TS-FEM), the machine performances are evaluated. Finally, the FT-PMV machine is manufactured, and the experimental results are presented to validate the theoretical analysis. PMID:25045729

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2014 CFR

2014-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2012 CFR

2012-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

32 CFR 518.20 - Collection of fees and fee rates.

Code of Federal Regulations, 2014 CFR

2014-07-01

...; individual time (hereafter referred to as human time), and machine time. (i) Human time. Human time is all the time spent by humans performing the necessary tasks to prepare the job for a machine to execute..., programmer, database administrator, or action officer). (ii) Machine time. Machine time involves only direct...

32 CFR 518.20 - Collection of fees and fee rates.

Code of Federal Regulations, 2012 CFR

2012-07-01

...; individual time (hereafter referred to as human time), and machine time. (i) Human time. Human time is all the time spent by humans performing the necessary tasks to prepare the job for a machine to execute..., programmer, database administrator, or action officer). (ii) Machine time. Machine time involves only direct...

32 CFR 518.20 - Collection of fees and fee rates.

Code of Federal Regulations, 2013 CFR

2013-07-01

...; individual time (hereafter referred to as human time), and machine time. (i) Human time. Human time is all the time spent by humans performing the necessary tasks to prepare the job for a machine to execute..., programmer, database administrator, or action officer). (ii) Machine time. Machine time involves only direct...

32 CFR 701.53 - FOIA fee schedule.

Code of Federal Regulations, 2013 CFR

2013-07-01

... human time) and machine time. (1) Human time. Human time is all the time spent by humans performing the...) Machine time. Machine time involves only direct costs of the central processing unit (CPU), input/output... exist to calculate CPU time, no machine costs can be passed on to the requester. When CPU calculations...

Heat engine generator control system

DOEpatents

Rajashekara, K.; Gorti, B.V.; McMullen, S.R.; Raibert, R.J.

1998-05-12

An electrical power generation system includes a heat engine having an output member operatively coupled to the rotor of a dynamoelectric machine. System output power is controlled by varying an electrical parameter of the dynamoelectric machine. A power request signal is related to an engine speed and the electrical parameter is varied in accordance with a speed control loop. Initially, the sense of change in the electrical parameter in response to a change in the power request signal is opposite that required to effectuate a steady state output power consistent with the power request signal. Thereafter, the electrical parameter is varied to converge the output member speed to the speed known to be associated with the desired electrical output power. 8 figs.

Heat engine generator control system

DOEpatents

Rajashekara, Kaushik; Gorti, Bhanuprasad Venkata; McMullen, Steven Robert; Raibert, Robert Joseph

1998-01-01

An electrical power generation system includes a heat engine having an output member operatively coupled to the rotor of a dynamoelectric machine. System output power is controlled by varying an electrical parameter of the dynamoelectric machine. A power request signal is related to an engine speed and the electrical parameter is varied in accordance with a speed control loop. Initially, the sense of change in the electrical parameter in response to a change in the power request signal is opposite that required to effectuate a steady state output power consistent with the power request signal. Thereafter, the electrical parameter is varied to converge the output member speed to the speed known to be associated with the desired electrical output power.

Optimization of design parameters for bulk micromachined silicon membranes for piezoresistive pressure sensing application

NASA Astrophysics Data System (ADS)

Belwanshi, Vinod; Topkar, Anita

2016-05-01

Finite element analysis study has been carried out to optimize the design parameters for bulk micro-machined silicon membranes for piezoresistive pressure sensing applications. The design is targeted for measurement of pressure up to 200 bar for nuclear reactor applications. The mechanical behavior of bulk micro-machined silicon membranes in terms of deflection and stress generation has been simulated. Based on the simulation results, optimization of the membrane design parameters in terms of length, width and thickness has been carried out. Subsequent to optimization of membrane geometrical parameters, the dimensions and location of the high stress concentration region for implantation of piezoresistors have been obtained for sensing of pressure using piezoresistive sensing technique.

Preprocessor with spline interpolation for converting stereolithography into cutter location source data

NASA Astrophysics Data System (ADS)

Nagata, Fusaomi; Okada, Yudai; Sakamoto, Tatsuhiko; Kusano, Takamasa; Habib, Maki K.; Watanabe, Keigo

2017-06-01

The authors have developed earlier an industrial machining robotic system for foamed polystyrene materials. The developed robotic CAM system provided a simple and effective interface without the need to use any robot language between operators and the machining robot. In this paper, a preprocessor for generating Cutter Location Source data (CLS data) from Stereolithography (STL data) is first proposed for robotic machining. The preprocessor enables to control the machining robot directly using STL data without using any commercially provided CAM system. The STL deals with a triangular representation for a curved surface geometry. The preprocessor allows machining robots to be controlled through a zigzag or spiral path directly calculated from STL data. Then, a smart spline interpolation method is proposed and implemented for smoothing coarse CLS data. The effectiveness and potential of the developed approaches are demonstrated through experiments on actual machining and interpolation.

Drilling High Precision Holes in Ti6Al4V Using Rotary Ultrasonic Machining and Uncertainties Underlying Cutting Force, Tool Wear, and Production Inaccuracies.

PubMed

Chowdhury, M A K; Sharif Ullah, A M M; Anwar, Saqib

2017-09-12

Ti6Al4V alloys are difficult-to-cut materials that have extensive applications in the automotive and aerospace industry. A great deal of effort has been made to develop and improve the machining operations of Ti6Al4V alloys. This paper presents an experimental study that systematically analyzes the effects of the machining conditions (ultrasonic power, feed rate, spindle speed, and tool diameter) on the performance parameters (cutting force, tool wear, overcut error, and cylindricity error), while drilling high precision holes on the workpiece made of Ti6Al4V alloys using rotary ultrasonic machining (RUM). Numerical results were obtained by conducting experiments following the design of an experiment procedure. The effects of the machining conditions on each performance parameter have been determined by constructing a set of possibility distributions (i.e., trapezoidal fuzzy numbers) from the experimental data. A possibility distribution is a probability-distribution-neural representation of uncertainty, and is effective in quantifying the uncertainty underlying physical quantities when there is a limited number of data points which is the case here. Lastly, the optimal machining conditions have been identified using these possibility distributions.

Application of grey-fuzzy approach in parametric optimization of EDM process in machining of MDN 300 steel

NASA Astrophysics Data System (ADS)

Protim Das, Partha; Gupta, P.; Das, S.; Pradhan, B. B.; Chakraborty, S.

2018-01-01

Maraging steel (MDN 300) find its application in many industries as it exhibits high hardness which are very difficult to machine material. Electro discharge machining (EDM) is an extensively popular machining process which can be used in machining of such materials. Optimization of response parameters are essential for effective machining of these materials. Past researchers have already used Taguchi for obtaining the optimal responses of EDM process for this material with responses such as material removal rate (MRR), tool wear rate (TWR), relative wear ratio (RWR), and surface roughness (SR) considering discharge current, pulse on time, pulse off time, arc gap, and duty cycle as process parameters. In this paper, grey relation analysis (GRA) with fuzzy logic is applied to this multi objective optimization problem to check the responses by an implementation of the derived parametric setting. It was found that the parametric setting derived by the proposed method results in better a response than those reported by the past researchers. Obtained results are also verified using the technique for order of preference by similarity to ideal solution (TOPSIS). The predicted result also shows that there is a significant improvement in comparison to the results of past researchers.

Machine processing of ERTS and ground truth data

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Peacock, K.

1973-01-01

The author has identified the following significant results. Results achieved by ERTS-Atmospheric Experiment PR303, whose objective is to establish a radiometric calibration technique, are reported. This technique, which determines and removes solar and atmospheric parameters that degrade the radiometric fidelity of ERTS-1 data, transforms the ERTS-1 sensor radiance measurements to absolute target reflectance signatures. A radiant power measuring instrument and its use in determining atmospheric parameters needed for ground truth are discussed. The procedures used and results achieved in machine processing ERTS-1 computer -compatible tapes and atmospheric parameters to obtain target reflectance are reviewed.

An Analysis of the Effects of Chip-groove Geometry on Machining Performance Using Finite Element Methods

NASA Astrophysics Data System (ADS)

Ee, K. C.; Dillon, O. W.; Jawahir, I. S.

2004-06-01

This paper discusses the influence of major chip-groove parameters of a cutting tool on the chip formation process in orthogonal machining using finite element (FE) methods. In the FE formulation, a thermal elastic-viscoplastic material model is used together with a modified Johnson-Cook material law for the flow stress. The chip back-flow angle and the chip up-curl radius are calculated for a range of cutting conditions by varying the chip-groove parameters. The analysis provides greater understanding of the effectiveness of chip-groove configurations and points a way to correlate cutting conditions with tool-wear when machining with a grooved cutting tool.

Parametric Optimization of Wire Electrical Discharge Machining of Powder Metallurgical Cold Worked Tool Steel using Taguchi Method

NASA Astrophysics Data System (ADS)

Sudhakara, Dara; Prasanthi, Guvvala

2017-04-01

Wire Cut EDM is an unconventional machining process used to build components of complex shape. The current work mainly deals with optimization of surface roughness while machining P/M CW TOOL STEEL by Wire cut EDM using Taguchi method. The process parameters of the Wire Cut EDM is ON, OFF, IP, SV, WT, and WP. L27 OA is used for to design of the experiments for conducting experimentation. In order to find out the effecting parameters on the surface roughness, ANOVA analysis is engaged. The optimum levels for getting minimum surface roughness is ON = 108 µs, OFF = 63 µs, IP = 11 A, SV = 68 V and WT = 8 g.

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

NASA Astrophysics Data System (ADS)

Govorov, Michael; Gienko, Gennady; Putrenko, Viktor

2018-05-01

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

Extreme ultraviolet lithography machine

DOEpatents

Tichenor, Daniel A.; Kubiak, Glenn D.; Haney, Steven J.; Sweeney, Donald W.

2000-01-01

An extreme ultraviolet lithography (EUVL) machine or system for producing integrated circuit (IC) components, such as transistors, formed on a substrate. The EUVL machine utilizes a laser plasma point source directed via an optical arrangement onto a mask or reticle which is reflected by a multiple mirror system onto the substrate or target. The EUVL machine operates in the 10-14 nm wavelength soft x-ray photon. Basically the EUV machine includes an evacuated source chamber, an evacuated main or project chamber interconnected by a transport tube arrangement, wherein a laser beam is directed into a plasma generator which produces an illumination beam which is directed by optics from the source chamber through the connecting tube, into the projection chamber, and onto the reticle or mask, from which a patterned beam is reflected by optics in a projection optics (PO) box mounted in the main or projection chamber onto the substrate. In one embodiment of a EUVL machine, nine optical components are utilized, with four of the optical components located in the PO box. The main or projection chamber includes vibration isolators for the PO box and a vibration isolator mounting for the substrate, with the main or projection chamber being mounted on a support structure and being isolated.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, K; Yu, Z; Chen, H

Purpose: To implement VMAT in RayStation with the Elekta Synergy linac with the new Agility MLC, and to utilize the same vendor softwares to determine the optimum Elekta VMAT machine parameters in RayStation for accurate modeling and robust delivery. Methods: iCOMCat is utilized to create various beam patterns with user defined dose rate, gantry, MLC and jaw speed for each control point. The accuracy and stability of the output and beam profile are qualified for each isolated functional component of VMAT delivery using ion chamber and Profiler2 with isocentric mounting fixture. Service graphing on linac console is used to verifymore » the mechanical motion accuracy. The determined optimum Elekta VMAT machine parameters were configured in RayStation v4.5.1. To evaluate the system overall performance, TG-119 test cases and nine retrospective VMAT patients were planned on RayStation, and validated using both ArcCHECK (with plug and ion chamber) and MapCHECK2. Results: Machine output and profile varies <0.3% when only variable is dose rate (35MU/min-600MU/min). <0.9% output and <0.3% profile variation are observed with additional gantry motion (0.53deg/s–5.8deg/s both directions). The output and profile variation are still <1% with additional slow leaf motion (<1.5cm/s both direction). However, the profile becomes less symmetric, and >1.5% output and 7% profile deviation is seen with >2.5cm/s leaf motion. All clinical cases achieved comparable plan quality as treated IMRT plans. The gamma passing rate is 99.5±0.5% on ArcCheck (<3% iso center dose deviation) and 99.1±0.8% on MapCheck2 using 3%/3mm gamma (10% lower threshold). Mechanical motion accuracy in all VMAT deliveries is <1°/1mm. Conclusion: Accurate RayStation modeling and robust VMAT delivery is achievable on Elekta Agility for <2.5cm/s leaf motion and full range of dose rate and gantry speed determined by the same vendor softwares. Our TG-119 and patient results have provided us with the confidence to use VMAT clinically.« less

Aqueous cutting fluid for machining fissionable materials

DOEpatents

Duerksen, Walter K.; Googin, John M.; Napier, Jr., Bradley

1984-01-01

The present invention is directed to a cutting fluid for machining fissionable material. The cutting fluid is formed of glycol, water and boron compound in an adequate concentration for effective neutron attenuation so as to inhibit criticality incidents during machining.

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine

PubMed Central

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam SM, Jahangir

2017-01-01

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems. PMID:28422080

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

PubMed

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

2017-04-19

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

Roundness and taper of holes during drilling composites of various thickness by HSS drill bit under dry condition

NASA Astrophysics Data System (ADS)

Sakib, M. S.; Rahman, Motiur; Ferdous, M.; Dhar, N. R.

2017-12-01

Polymer Matrix Composites are extending a wide range of applications in aviation in recent eras because of their better economics, well established processing, high temperature properties, high resistance to corrosion and fatigue. Directional properties of composites are dependent on the fibre orientation. Composites being anisotropic in nature are difficult to drill and machining and tooling of the composites remained a great challenge over time. This paper addresses the issues of various machining problems such as delamination, fibre pull-out, cracks on varying drilling parameters like feed rate and drilling speed. Experimental drilling was carried out on Fibre Reinforced Plastic composites with HSS drill bit. Results reveal that as the number of holes increases the entry and exit diameter and tapper of holes vary and also varying composite thickness results in a difference in hole roundness and tapper. This experiment summarizes that for achieving acceptable tool life and hole quality demands a drill designed with composites.

Assessment of microwave-based clinical waste decontamination unit.

PubMed

Hoffman, P N; Hanley, M J

1994-12-01

A clinical waste decontamination unit that used microwave-generated heat was assessed for operator safety and efficacy. Tests with loads artificially contaminated with aerosol-forming particles showed that no particles were detected outside the machine provided the seals and covers were correctly seated. Thermometric measurement of a self-generated steam decontamination cycle was used to determine the parameters needed to ensure heat disinfection of the waste reception hopper, prior to entry for maintenance or repair. Bacterial and thermometric test pieces were passed through the machine within a full load of clinical waste. These test pieces, designed to represent a worst case situation, were enclosed in aluminium foil to shield them from direct microwave energy. None of the 100 bacterial test pieces yielded growth on culture and all 100 thermal test pieces achieved temperatures in excess of 99 degrees C during their passage through the decontamination unit. It was concluded that this method may be used to render safe the bulk of of ward-generated clinical waste.

Machine learning assembly landscapes from particle tracking data.

PubMed

Long, Andrew W; Zhang, Jie; Granick, Steve; Ferguson, Andrew L

2015-11-07

Bottom-up self-assembly offers a powerful route for the fabrication of novel structural and functional materials. Rational engineering of self-assembling systems requires understanding of the accessible aggregation states and the structural assembly pathways. In this work, we apply nonlinear machine learning to experimental particle tracking data to infer low-dimensional assembly landscapes mapping the morphology, stability, and assembly pathways of accessible aggregates as a function of experimental conditions. To the best of our knowledge, this represents the first time that collective order parameters and assembly landscapes have been inferred directly from experimental data. We apply this technique to the nonequilibrium self-assembly of metallodielectric Janus colloids in an oscillating electric field, and quantify the impact of field strength, oscillation frequency, and salt concentration on the dominant assembly pathways and terminal aggregates. This combined computational and experimental framework furnishes new understanding of self-assembling systems, and quantitatively informs rational engineering of experimental conditions to drive assembly along desired aggregation pathways.

Technical Report: Reference photon dosimetry data for Varian accelerators based on IROC-Houston site visit data.

PubMed

Kerns, James R; Followill, David S; Lowenstein, Jessica; Molineu, Andrea; Alvarez, Paola; Taylor, Paige A; Stingo, Francesco C; Kry, Stephen F

2016-05-01

Accurate data regarding linear accelerator (Linac) radiation characteristics are important for treatment planning system modeling as well as regular quality assurance of the machine. The Imaging and Radiation Oncology Core-Houston (IROC-H) has measured the dosimetric characteristics of numerous machines through their on-site dosimetry review protocols. Photon data are presented and can be used as a secondary check of acquired values, as a means to verify commissioning a new machine, or in preparation for an IROC-H site visit. Photon data from IROC-H on-site reviews from 2000 to 2014 were compiled and analyzed. Specifically, data from approximately 500 Varian machines were analyzed. Each dataset consisted of point measurements of several dosimetric parameters at various locations in a water phantom to assess the percentage depth dose, jaw output factors, multileaf collimator small field output factors, off-axis factors, and wedge factors. The data were analyzed by energy and parameter, with similarly performing machine models being assimilated into classes. Common statistical metrics are presented for each machine class. Measurement data were compared against other reference data where applicable. Distributions of the parameter data were shown to be robust and derive from a student's t distribution. Based on statistical and clinical criteria, all machine models were able to be classified into two or three classes for each energy, except for 6 MV for which there were eight classes. Quantitative analysis of the measurements for 6, 10, 15, and 18 MV photon beams is presented for each parameter; supplementary material has also been made available which contains further statistical information. IROC-H has collected numerous data on Varian Linacs and the results of photon measurements from the past 15 years are presented. The data can be used as a comparison check of a physicist's acquired values. Acquired values that are well outside the expected distribution should be verified by the physicist to identify whether the measurements are valid. Comparison of values to this reference data provides a redundant check to help prevent gross dosimetric treatment errors.

AFM surface imaging of AISI D2 tool steel machined by the EDM process

NASA Astrophysics Data System (ADS)

Guu, Y. H.

2005-04-01

The surface morphology, surface roughness and micro-crack of AISI D2 tool steel machined by the electrical discharge machining (EDM) process were analyzed by means of the atomic force microscopy (AFM) technique. Experimental results indicate that the surface texture after EDM is determined by the discharge energy during processing. An excellent machined finish can be obtained by setting the machine parameters at a low pulse energy. The surface roughness and the depth of the micro-cracks were proportional to the power input. Furthermore, the AFM application yielded information about the depth of the micro-cracks is particularly important in the post treatment of AISI D2 tool steel machined by EDM.

Survey of beam instrumentation used in SLC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ecklund, S.D.

A survey of beam instruments used at SLAC in the SLC machine is presented. The basic utility and operation of each device is briefly described. The various beam instruments used at the Stanford Linear Collider (SLC), can be classified by the function they perform. Beam intensity, position and size are typical of the parameters of beam which are measured. Each type of parameter is important for adjusting or tuning the machine in order to achieve optimum performance. 39 refs.

Physical mechanism of ultrasonic machining

NASA Astrophysics Data System (ADS)

Isaev, A.; Grechishnikov, V.; Kozochkin, M.; Pivkin, P.; Petuhov, Y.; Romanov, V.

2016-04-01

In this paper, the main aspects of ultrasonic machining of constructional materials are considered. Influence of coolant on surface parameters is studied. Results of experiments on ultrasonic lathe cutting with application of tangential vibrations and with use of coolant are considered.

Application of Taguchi-grey method to optimize drilling of EMS 45 steel using minimum quantity lubrication (MQL) with multiple performance characteristics

NASA Astrophysics Data System (ADS)

Soepangkat, Bobby O. P.; Suhardjono, Pramujati, Bambang

2017-06-01

Machining under minimum quantity lubrication (MQL) has drawn the attention of researchers as an alternative to the traditionally used wet and dry machining conditions with the purpose to minimize the cooling and lubricating cost, as well as to reduce cutting zone temperature, tool wear, and hole surface roughness. Drilling is one of the important operations to assemble machine components. The objective of this study was to optimize drilling parameters such as cutting feed and cutting speed, drill type and drill point angle on the thrust force, torque, hole surface roughness and tool flank wear in drilling EMS 45 tool steel using MQL. In this study, experiments were carried out as per Taguchi design of experiments while an L18 orthogonal array was used to study the influence of various combinations of drilling parameters and tool geometries on the thrust force, torque, hole surface roughness and tool flank wear. The optimum drilling parameters was determined by using grey relational grade obtained from grey relational analysis for multiple-performance characteristics. The drilling experiments were carried out by using twist drill and CNC machining center. This work is useful for optimum values selection of various drilling parameters and tool geometries that would not only minimize the thrust force and torque, but also reduce hole surface roughness and tool flank wear.

The value of prior knowledge in machine learning of complex network systems.

PubMed

Ferranti, Dana; Krane, David; Craft, David

2017-11-15

Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects'). Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd. dcraft@broadinstitute.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Technology of machine tools. Volume 4. Machine tool controls

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 3. Machine tool mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tlusty, J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 5. Machine tool accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hocken, R.J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Biomachining: metal etching via microorganisms.

PubMed

Díaz-Tena, Estíbaliz; Barona, Astrid; Gallastegui, Gorka; Rodríguez, Adrián; López de Lacalle, L Norberto; Elías, Ana

2017-05-01

The use of microorganisms to remove metal from a workpiece is known as biological machining or biomachining, and it has gained in both importance and scientific relevance over the past decade. Conversely to mechanical methods, the use of readily available microorganisms is low-energy consuming, and no thermal damage is caused during biomachining. The performance of this sustainable process is assessed by the material removal rate, and certain parameters have to be controlled for manufacturing the machined part with the desired surface finish. Although the variety of microorganisms is scarce, cell concentration or density plays an important role in the process. There is a need to control the temperature to maintain microorganism activity at its optimum, and a suitable shaking rate provides an efficient contact between the workpiece and the biological medium. The system's tolerance to the sharp changes in pH is quite limited, and in many cases, an acid medium has to be maintained for effective performance. This process is highly dependent on the type of metal being removed. Consequently, the operating parameters need to be determined on a case-by-case basis. The biomachining time is another variable with a direct impact on the removal rate. This biological technique can be used for machining simple and complex shapes, such as series of linear, circular, and square micropatterns on different metal surfaces. The optimal biomachining process should be fast enough to ensure high production, a smooth and homogenous surface finish and, in sum, a high-quality piece. As a result of the high global demand for micro-components, biomachining provides an effective and sustainable alternative. However, its industrial-scale implementation is still pending.

The construction of support vector machine classifier using the firefly algorithm.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

Computational Fluid Dynamic Simulation of Flow in Abrasive Water Jet Machining

NASA Astrophysics Data System (ADS)

Venugopal, S.; Sathish, S.; Jothi Prakash, V. M.; Gopalakrishnan, T.

2017-03-01

Abrasive water jet cutting is one of the most recently developed non-traditional manufacturing technologies. In this machining, the abrasives are mixed with suspended liquid to form semi liquid mixture. The general nature of flow through the machining, results in fleeting wear of the nozzle which decrease the cutting performance. The inlet pressure of the abrasive water suspension has main effect on the major destruction characteristics of the inner surface of the nozzle. The aim of the project is to analyze the effect of inlet pressure on wall shear and exit kinetic energy. The analysis could be carried out by changing the taper angle of the nozzle, so as to obtain optimized process parameters for minimum nozzle wear. The two phase flow analysis would be carried by using computational fluid dynamics tool CFX. It is also used to analyze the flow characteristics of abrasive water jet machining on the inner surface of the nozzle. The availability of optimized process parameters of abrasive water jet machining (AWJM) is limited to water and experimental test can be cost prohibitive. In this case, Computational fluid dynamics analysis would provide better results.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

NASA Astrophysics Data System (ADS)

Mohan Reddy, M.; Gorin, Alexander; Abou-El-Hossein, K. A.

2011-02-01

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

30 CFR 75.703-2 - Approved grounding mediums.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Approved grounding mediums. 75.703-2 Section 75... mediums. For purposes of grounding offtrack direct-current machines, the following grounding mediums are... alternating current grounding medium where such machines are fed by an ungrounded direct-current power system...

30 CFR 75.703-2 - Approved grounding mediums.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Approved grounding mediums. 75.703-2 Section 75... mediums. For purposes of grounding offtrack direct-current machines, the following grounding mediums are... alternating current grounding medium where such machines are fed by an ungrounded direct-current power system...

30 CFR 75.703-2 - Approved grounding mediums.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Approved grounding mediums. 75.703-2 Section 75... mediums. For purposes of grounding offtrack direct-current machines, the following grounding mediums are... alternating current grounding medium where such machines are fed by an ungrounded direct-current power system...

30 CFR 75.703-2 - Approved grounding mediums.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Approved grounding mediums. 75.703-2 Section 75... mediums. For purposes of grounding offtrack direct-current machines, the following grounding mediums are... alternating current grounding medium where such machines are fed by an ungrounded direct-current power system...

30 CFR 75.703-2 - Approved grounding mediums.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Approved grounding mediums. 75.703-2 Section 75... mediums. For purposes of grounding offtrack direct-current machines, the following grounding mediums are... alternating current grounding medium where such machines are fed by an ungrounded direct-current power system...

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Solving a Higgs optimization problem with quantum annealing for machine learning

NASA Astrophysics Data System (ADS)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-01

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Study on Performance of Integration Control by Man and Machine in Stage of Final Approaching for Spaceship Rendezvous and Docking

NASA Astrophysics Data System (ADS)

Zhou, Qianxiang; Liu, Zhongqi

With the development of manned space technology, space rendezvous and docking (RVD) technology will play a more and more important role. The astronauts’ participation in a final close period of man-machine combination control is an important way of RVD technology. Spacecraft RVD control involves control problem of a total of 12 degrees of freedom (location) and attitude which it relative to the inertial space the orbit. Therefore, in order to reduce the astronauts’ operation load and reduce the security requirements to the ground station and achieve an optimal performance of the whole man-machine system, it is need to study how to design the number of control parameters of astronaut or aircraft automatic control system. In this study, with the laboratory conditions on the ground, a method was put forward to develop an experimental system in which the performance evaluation of spaceship RVD integration control by man and machine could be completed. After the RVD precision requirements were determined, 26 male volunteers aged 20-40 took part in the performance evaluation experiments. The RVD integration control success rates and total thruster ignition time were chosen as evaluation indices. Results show that if less than three RVD parameters control tasks were finished by subject and the rest of parameters control task completed by automation, the RVD success rate would be larger than eighty-eight percent and the fuel consumption would be optimized. In addition, there were two subjects who finished the whole six RVD parameters control tasks by enough train. In conclusion, if the astronauts' role should be integrated into the RVD control, it was suitable for them to finish the heading, pitch and roll control in order to assure the man-machine system high performance. If astronauts were needed to finish all parameter control, two points should be taken into consideration, one was enough fuel and another was enough long operation time.

Prediction of 3D chip formation in the facing cutting with lathe machine using FEM

NASA Astrophysics Data System (ADS)

Prasetyo, Yudhi; Tauviqirrahman, Mohamad; Rusnaldy

2016-04-01

This paper presents the prediction of the chip formation at the machining process using a lathe machine in a more specific way focusing on facing cutting (face turning). The main purpose is to propose a new approach to predict the chip formation with the variation of the cutting directions i.e., the backward and forward direction. In addition, the interaction between stress analysis and chip formation on cutting process was also investigated. The simulations were conducted using three dimensional (3D) finite element method based on ABAQUS software with aluminum and high speed steel (HSS) as the workpiece and the tool materials, respectively. The simulation result showed that the chip resulted using a backward direction depicts a better formation than that using a conventional (forward) direction.

Engineering of the `PCAST machine`

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sinnis, J.; Brooks, A.; Brown, T.

The President`s Committee of Advisors on Science and Technology (PCAST) has suggested that a device with a mission of ignition and moderate burn time could address the physics of burning plasmas at a lesser cost than ITER with its more comprehensive physics and technology mission. The Department of Energy commissioned a study to explore this PCAST suggestion. This paper describes the results of the engineering portion of the study of this `PCAST Machine;` physics is covered in a companion paper authored by G.H. Neilson, et al; and the costs are covered in a companion paper by R.T. Simmons, et al.more » Both are published in the proceedings of this conference. The study was undertaken by a team under the direction of Bruce Montgomery that included representatives from MIT, PPPL, ORNL, LLNL, GA, Northrup-Grumman, and Stone and Webster. The performance requirements for the PCAST machine are to form and sustain a burning plasma for three helium accumulation times. The philosophy adopted for this design was to achieve the required performance at lower cost by decreasing the major radius to five meters, increasing the toroidal field to 7 tesla, and using stronger shaping. The major device parameters are given. 4 refs., 4 figs., 1 tab.« less

Optimization of Coolant Technique Conditions for Machining A319 Aluminium Alloy Using Response Surface Method (RSM)

NASA Astrophysics Data System (ADS)

Zainal Ariffin, S.; Razlan, A.; Ali, M. Mohd; Efendee, A. M.; Rahman, M. M.

2018-03-01

Background/Objectives: The paper discusses about the optimum cutting parameters with coolant techniques condition (1.0 mm nozzle orifice, wet and dry) to optimize surface roughness, temperature and tool wear in the machining process based on the selected setting parameters. The selected cutting parameters for this study were the cutting speed, feed rate, depth of cut and coolant techniques condition. Methods/Statistical Analysis Experiments were conducted and investigated based on Design of Experiment (DOE) with Response Surface Method. The research of the aggressive machining process on aluminum alloy (A319) for automotive applications is an effort to understand the machining concept, which widely used in a variety of manufacturing industries especially in the automotive industry. Findings: The results show that the dominant failure mode is the surface roughness, temperature and tool wear when using 1.0 mm nozzle orifice, increases during machining and also can be alternative minimize built up edge of the A319. The exploration for surface roughness, productivity and the optimization of cutting speed in the technical and commercial aspects of the manufacturing processes of A319 are discussed in automotive components industries for further work Applications/Improvements: The research result also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms. According to the mathematical model and equations, generated by CCD based RSM, experiments were performed and cutting coolant condition technique using size nozzle can reduces tool wear, surface roughness and temperature was obtained. Results have been analyzed and optimization has been carried out for selecting cutting parameters, shows that the effectiveness and efficiency of the system can be identified and helps to solve potential problems.

Melt-Pool Temperature and Size Measurement During Direct Laser Sintering

DOE Office of Scientific and Technical Information (OSTI.GOV)

List, III, Frederick Alyious; Dinwiddie, Ralph Barton; Carver, Keith

2017-08-01

Additive manufacturing has demonstrated the ability to fabricate complex geometries and components not possible with conventional casting and machining. In many cases, industry has demonstrated the ability to fabricate complex geometries with improved efficiency and performance. However, qualification and certification of processes is challenging, leaving companies to focus on certification of material though design allowable based approaches. This significantly reduces the business case for additive manufacturing. Therefore, real time monitoring of the melt pool can be used to detect the development of flaws, such as porosity or un-sintered powder and aid in the certification process. Characteristics of the melt poolmore » in the Direct Laser Sintering (DLS) process is also of great interest to modelers who are developing simulation models needed to improve and perfect the DLS process. Such models could provide a means to rapidly develop the optimum processing parameters for new alloy powders and optimize processing parameters for specific part geometries. Stratonics’ ThermaViz system will be integrated with the Renishaw DLS system in order to demonstrate its ability to measure melt pool size, shape and temperature. These results will be compared with data from an existing IR camera to determine the best approach for the determination of these critical parameters.« less

Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach

NASA Astrophysics Data System (ADS)

Rudrapati, R.; Sahoo, P.; Bandyopadhyay, A.

2016-09-01

The main aim of the present work is to analyse the significance of turning parameters on surface roughness in computer numerically controlled (CNC) turning operation while machining of aluminium alloy material. Spindle speed, feed rate and depth of cut have been considered as machining parameters. Experimental runs have been conducted as per Box-Behnken design method. After experimentation, surface roughness is measured by using stylus profile meter. Factor effects have been studied through analysis of variance. Mathematical modelling has been done by response surface methodology, to made relationships between the input parameters and output response. Finally, process optimization has been made by teaching learning based optimization (TLBO) algorithm. Predicted turning condition has been validated through confirmatory experiment.

Integrated intelligent sensor for the textile industry

NASA Astrophysics Data System (ADS)

Peltie, Philippe; David, Dominique

1996-08-01

A new sensor has been developed for pantyhose inspection. Unlike a first complete inspection machine devoted to post- manufacturing control of the whole panty, this sensor will be directly integrated on currently existing manufacturing machines, and will combine advantages of miniaturization is to design an intelligent, compact and very cheap product, which should be integrated without requiring any modifications of host machines. The sensor part was designed to achieve closed acquisition, and various solutions have been explored to maintain adequate depth of field. The illumination source will be integrated in the device. The processing part will include correction facilities and electronic processing. Finally, high-level information will be output in order to interface directly with the manufacturing machine automate.

Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

NASA Astrophysics Data System (ADS)

Negro Maggio, Valentina; Iocchi, Luca

2015-02-01

Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

Technology of machine tools. Volume 2. Machine tool systems management and utilization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomson, A.R.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

Multi-Stage Convex Relaxation Methods for Machine Learning

DTIC Science & Technology

2013-03-01

Many problems in machine learning can be naturally formulated as non-convex optimization problems. However, such direct nonconvex formulations have...original nonconvex formulation. We will develop theoretical properties of this method and algorithmic consequences. Related convex and nonconvex machine learning methods will also be investigated.

24 CFR 3280.607 - Plumbing fixtures.

Code of Federal Regulations, 2014 CFR

2014-04-01

... two or more compartments, dishwashers, clothes washing machines, laundry tubs, bath tubs, and not less... for Safety Performance Specifications and Methods of Test for Safety Glazing Materials Used in...) Dishwashing machines. (i) A dishwashing machine shall not be directly connected to any waste piping, but shall...

Temperature Measurement and Numerical Prediction in Machining Inconel 718.

PubMed

Díaz-Álvarez, José; Tapetado, Alberto; Vázquez, Carmen; Miguélez, Henar

2017-06-30

Thermal issues are critical when machining Ni-based superalloy components designed for high temperature applications. The low thermal conductivity and extreme strain hardening of this family of materials results in elevated temperatures around the cutting area. This elevated temperature could lead to machining-induced damage such as phase changes and residual stresses, resulting in reduced service life of the component. Measurement of temperature during machining is crucial in order to control the cutting process, avoiding workpiece damage. On the other hand, the development of predictive tools based on numerical models helps in the definition of machining processes and the obtainment of difficult to measure parameters such as the penetration of the heated layer. However, the validation of numerical models strongly depends on the accurate measurement of physical parameters such as temperature, ensuring the calibration of the model. This paper focuses on the measurement and prediction of temperature during the machining of Ni-based superalloys. The temperature sensor was based on a fiber-optic two-color pyrometer developed for localized temperature measurements in turning of Inconel 718. The sensor is capable of measuring temperature in the range of 250 to 1200 °C. Temperature evolution is recorded in a lathe at different feed rates and cutting speeds. Measurements were used to calibrate a simplified numerical model for prediction of temperature fields during turning.

Investigation of Machine-ability of Inconel 800 in EDM with Coated Electrode

NASA Astrophysics Data System (ADS)

Karunakaran, K.; Chandrasekaran, M.

2017-03-01

The Inconel 800 is a high temperature application alloy which is classified as a nickel based super alloy. It has wide scope in aerospace engineering, gas Turbine etc. The machine-ability studies were found limited on this material. Hence This research focuses on machine-ability studies on EDM of Inconel 800 with Silver Coated Electrolyte Copper Electrode. The purpose of coating on electrode is to reduce tool wear. The factors pulse on Time, Pulse off Time and Peck Current were considered to observe the responses of surface roughness, material removal rate, tool wear rate. Taguchi Full Factorial Design is employed for Design the experiment. Some specific findings were reported and the percentage of contribution of each parameter was furnished

A sandwiched piezoelectric transducer with flex end-caps for energy harvesting in large force environments

NASA Astrophysics Data System (ADS)

Kuang, Yang; Daniels, Alice; Zhu, Meiling

2017-08-01

This paper presents a sandwiched piezoelectric transducer (SPT) for energy harvesting in large force environments with increased load capacity and electric power output. The SPT uses (1) flex end-caps to amplify the applied load force so as to increase its power output and (2) a sandwiched piezoelectric-substrate structure to reduce the stress concentration in the piezoelectric material so as to increase the load capacity. A coupled piezoelectric-circuit finite element model (CPC-FEM) was developed, which is able to directly predict the electric power output of the SPT connected to a load resistor. The CPC-FEM was used to study the effects of various parameters of the SPT on the performance to obtain an optimal design. These parameters included the substrate thickness, the end-cap material and thickness, the electrode length, the joint length, the end-cap internal angle and the PZT thickness. A prototype with optimised parameters was tested on a loading machine, and the experimental results were compared with simulation. A good agreement was observed between simulation and experiment. When subjected to a 1 kN 2 Hz sinusoidal force applied by the loading machine, the SPT produced an average power of 4.68 mW. The application of the SPT as a footwear energy harvester was demonstrated by fitting the SPT into a boot and performing the tests on a treadmill, and the SPT generated an average power of 2.5 mW at a walking speed of 4.8 km h-1.

A methodology for optimization of wind farm allocation under land restrictions: the case of the Canary Islands

NASA Astrophysics Data System (ADS)

Castaño Moraga, C. A.; Suárez Santana, E.; Sabbagh Rodríguez, I.; Nebot Medina, R.; Suárez García, S.; Rodríguez Alvarado, J.; Piernavieja Izquierdo, G.; Ruiz Alzola, J.

2010-09-01

Wind farms authorization and power allocations to private investors promoting wind energy projects requires some planification strategies. This issue is even more important under land restrictions, as it is the case of Canary Islands, where numerous specially protected areas are present for environmental reasons and land is a scarce resource. Aware of this limitation, the Regional Government of Canary Islands designed the requirements of a public tender to grant licences to install new wind farms trying to maximize the energy produced in terms of occupied land. In this paper, we detail the methodology developed by the Canary Islands Institute of Technology (ITC, S.A.) to support the work of the technical staff of the Regional Ministry of Industry, responsible for the evaluation of a competitive tender process for awarding power lincenses to private investors. The maximization of wind energy production per unit of area requires an exhaustive wind profile characterization. To that end, wind speed was statistically characterized by means of a Weibull probability density function, which mainly depends on two parameters: the shape parameter K, which determines the slope of the curve, and the average wind speed v , which is a scale parameter. These two parameters have been evaluated at three different heights (40,60,80 m) over the whole canarian archipelago, as well as the main wind speed direction. These parameters are available from the public data source Wind Energy Map of the Canary Islands [1]. The proposed methodology is based on the calculation of an initially defined Energy Efficiency Basic Index (EEBI), which is a performance criteria that weighs the annual energy production of a wind farm per unit of area. The calculation of this parameter considers wind conditions, windturbine characteristics, geometry of windturbine distribution in the wind farm (position within the row and column of machines), and involves four steps: Estimation of the energy produced by every windturbine as if it were isolated from all the other machines of the wind farm, using its power curve and the statistical characterization of the wind profile at the site. Estimation of energy losses due to affections caused by other windturbine in the same row and missalignment with respect to the main wind speed direction. Estimation of energy losses due to affections induced by windturbines located upstream. EEBI calculation as the ratio between the annual energy production and the area occupied by the wind farm, as a function of wind speed profile and wind turbine characteristics. Computations involved above are modeled under a System Theory characterization

Calculation of parameters of technological equipment for deep-sea mining

NASA Astrophysics Data System (ADS)

Yungmeister, D. A.; Ivanov, S. E.; Isaev, A. I.

2018-03-01

The actual problem of extracting minerals from the bottom of the world ocean is considered. On the ocean floor, three types of minerals are of interest: iron-manganese concretions (IMC), cobalt-manganese crusts (CMC) and sulphides. The analysis of known designs of machines and complexes for the extraction of IMC is performed. These machines are based on the principle of excavating the bottom surface; however such methods do not always correspond to “gentle” methods of mining. The ecological purity of such mining methods does not meet the necessary requirements. Such machines require the transmission of high electric power through the water column, which in some cases is a significant challenge. The authors analyzed the options of transportation of the extracted mineral from the bottom. The paper describes the design of machines that collect IMC by the method of vacuum suction. In this method, the gripping plates or drums are provided with cavities in which a vacuum is created and individual IMC are attracted to the devices by a pressure drop. The work of such machines can be called “gentle” processing technology of the bottom areas. Their environmental impact is significantly lower than mechanical devices that carry out the raking of IMC. The parameters of the device for lifting the IMC collected on the bottom are calculated. With the use of Kevlar ropes of serial production up to 0.06 meters in diameter, with a cycle time of up to 2 hours and a lifting speed of up to 3 meters per second, a productivity of about 400,000 tons per year can be realized for IMC. The development of machines based on the calculated parameters and approbation of their designs will create a unique complex for the extraction of minerals at oceanic deposits.

Study on effect of tool electrodes on surface finish during electrical discharge machining of Nitinol

NASA Astrophysics Data System (ADS)

Sahu, Anshuman Kumar; Chatterjee, Suman; Nayak, Praveen Kumar; Sankar Mahapatra, Siba

2018-03-01

Electrical discharge machining (EDM) is a non-traditional machining process which is widely used in machining of difficult-to-machine materials. EDM process can produce complex and intrinsic shaped component made of difficult-to-machine materials, largely applied in aerospace, biomedical, die and mold making industries. To meet the required applications, the EDMed components need to possess high accuracy and excellent surface finish. In this work, EDM process is performed using Nitinol as work piece material and AlSiMg prepared by selective laser sintering (SLS) as tool electrode along with conventional copper and graphite electrodes. The SLS is a rapid prototyping (RP) method to produce complex metallic parts by additive manufacturing (AM) process. Experiments have been carried out varying different process parameters like open circuit voltage (V), discharge current (Ip), duty cycle (τ), pulse-on-time (Ton) and tool material. The surface roughness parameter like average roughness (Ra), maximum height of the profile (Rt) and average height of the profile (Rz) are measured using surface roughness measuring instrument (Talysurf). To reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L27 orthogonal array has been chosen. The surface properties of the EDM specimen are optimized by desirability function approach and the best parametric setting is reported for the EDM process. Type of tool happens to be the most significant parameter followed by interaction of tool type and duty cycle, duty cycle, discharge current and voltage. Better surface finish of EDMed specimen can be obtained with low value of voltage (V), discharge current (Ip), duty cycle (τ) and pulse on time (Ton) along with the use of AlSiMg RP electrode.

PredicT-ML: a tool for automating machine learning model building with big clinical data.

PubMed

Luo, Gang

2016-01-01

Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs. This paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data. The paper presents the detailed design of PredicT-ML. PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

Optimization of Machining Process Parameters for Surface Roughness of Al-Composites

NASA Astrophysics Data System (ADS)

Sharma, S.

2013-10-01

Metal matrix composites (MMCs) have become a leading material among the various types of composite materials for different applications due to their excellent engineering properties. Among the various types of composites materials, aluminum MMCs have received considerable attention in automobile and aerospace applications. These materials are known as the difficult-to-machine materials because of the hardness and abrasive nature of reinforcement element-like silicon carbide particles. In the present investigation Al-SiC composite was produced by stir casting process. The Brinell hardness of the alloy after SiC addition had increased from 74 ± 2 to 95 ± 5 respectively. The composite was machined using CNC turning center under different machining parameters such as cutting speed (S), feed rate (F), depth of cut (D) and nose radius (R). The effect of machining parameters on surface roughness (Ra) was studied using response surface methodology. Face centered composite design with three levels of each factor was used for surface roughness study of the developed composite. A response surface model for surface roughness was developed in terms of main factors (S, F, D and R) and their significant interactions (SD, SR, FD and FR). The developed model was validated by conducting experiments under different conditions. Further the model was optimized for minimum surface roughness. An error of 3-7 % was observed in the modeled and experimental results. Further, it was fond that the surface roughness of Al-alloy at optimum conditions is lower than that of Al-SiC composite.

Aquifer Hydrogeologic Layer Zonation at the Hanford Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Savelieva-Trofimova, Elena A.; Kanevski, Mikhail; timonin, v.

2003-09-10

Sedimentary aquifer layers are characterized by spatial variability of hydraulic properties. Nevertheless, zones with similar values of hydraulic parameters (parameter zones) can be distinguished. This parameter zonation approach is an alternative to the analysis of spatial variation of the continuous hydraulic parameters. The parameter zonation approach is primarily motivated by the lack of measurements that would be needed for direct spatial modeling of the hydraulic properties. The current work is devoted to the problem of zonation of the Hanford formation, the uppermost sedimentary aquifer unit (U1) included in hydrogeologic models at the Hanford site. U1 is characterized by 5 zonesmore » with different hydraulic properties. Each sampled location is ascribed to a parameter zone by an expert. This initial classification is accompanied by a measure of quality (also indicated by an expert) that addresses the level of classification confidence. In the current study, the coneptual zonation map developed by an expert geologist was used as an a priori model. The parameter zonation problem was formulated as a multiclass classification task. Different geostatistical and machine learning algorithms were adapted and applied to solve this problem, including: indicator kriging, conditional simulations, neural networks of different architectures, and support vector machines. All methods were trained using additional soft information based on expert estimates. Regularization methods were used to overcome possible overfitting. The zonation problem was complicated because there were few samples for some zones (classes) and by the spatial non-stationarity of the data. Special approaches were developed to overcome these complications. The comparison of different methods was performed using qualitative and quantitative statistical methods and image analysis. We examined the correspondence of the results with the geologically based interpretation, including the reproduction of the spatial orientation of the different classes and the spatial correlation structure of the classes. The uncertainty of the classification task was examined using both probabilistic interpretation of the estimators and by examining the results of a set of stochastic realizations. Characterization of the classification uncertainty is the main advantage of the proposed methods.« less

Principle of maximum entropy for reliability analysis in the design of machine components

NASA Astrophysics Data System (ADS)

Zhang, Yimin

2018-03-01

We studied the reliability of machine components with parameters that follow an arbitrary statistical distribution using the principle of maximum entropy (PME). We used PME to select the statistical distribution that best fits the available information. We also established a probability density function (PDF) and a failure probability model for the parameters of mechanical components using the concept of entropy and the PME. We obtained the first four moments of the state function for reliability analysis and design. Furthermore, we attained an estimate of the PDF with the fewest human bias factors using the PME. This function was used to calculate the reliability of the machine components, including a connecting rod, a vehicle half-shaft, a front axle, a rear axle housing, and a leaf spring, which have parameters that typically follow a non-normal distribution. Simulations were conducted for comparison. This study provides a design methodology for the reliability of mechanical components for practical engineering projects.

Design, analysis, and testing of a flexure-based vibration-assisted polishing device

NASA Astrophysics Data System (ADS)

Gu, Yan; Zhou, Yan; Lin, Jieqiong; Lu, Mingming; Zhang, Chenglong; Chen, Xiuyuan

2018-05-01

A vibration-assisted polishing device (VAPD) composed of leaf-spring and right-circular flexure hinges is proposed with the aim of realizing vibration-assisted machining along elliptical trajectories. To design the structure, energy methods and the finite-element method are used to calculate the performance of the proposed VAPD. An improved bacterial foraging optimization algorithm is used to optimize the structural parameters. In addition, the performance of the VAPD is tested experimentally. The experimental results indicate that the maximum strokes of the two directional mechanisms operating along the Z1 and Z2 directions are 29.5 μm and 29.3 μm, respectively, and the maximum motion resolutions are 10.05 nm and 10.01 nm, respectively. The maximum working bandwidth is 1,879 Hz, and the device has a good step response.

Neural Network Emulation of Reionization Simulations

NASA Astrophysics Data System (ADS)

Schmit, Claude J.; Pritchard, Jonathan R.

2018-05-01

Next generation radio experiments such as LOFAR, HERA and SKA are expected to probe the Epoch of Reionization and claim a first direct detection of the cosmic 21cm signal within the next decade. One of the major challenges for these experiments will be dealing with enormous incoming data volumes. Machine learning is key to increasing our data analysis efficiency. We consider the use of an artificial neural network to emulate 21cmFAST simulations and use it in a Bayesian parameter inference study. We then compare the network predictions to a direct evaluation of the EoR simulations and analyse the dependence of the results on the training set size. We find that the use of a training set of size 100 samples can recover the error contours of a full scale MCMC analysis which evaluates the model at each step.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hussain, A

Purpose: Novel linac machines, TrueBeam (TB) and Elekta Versa have updated head designing and software control system, include flattening-filter-free (FFF) photon and electron beams. Later on FFF beams were also introduced on C-Series machines. In this work FFF beams for same energy 6MV but from different machine versions were studied with reference to beam data parameters. Methods: The 6MV-FFF percent depth doses, profile symmetry and flatness, dose rate tables, and multi-leaf collimator (MLC) transmission factors were measured during commissioning process of both C-series and Truebeam machines. The scanning and dosimetric data for 6MV-FFF beam from Truebeam and C-Series linacs wasmore » compared. A correlation of 6MV-FFF beam from Elekta Versa with that of Varian linacs was also found. Results: The scanning files were plotted for both qualitative and quantitative analysis. The dosimetric leaf gap (DLG) for C-Series 6MV-FFF beam is 1.1 mm. Published values for Truebeam dosimetric leaf gap is 1.16 mm. 6MV MLC transmission factor varies between 1.3 % and 1.4 % in two separate measurements and measured DLG values vary between 1.32 mm and 1.33 mm on C-Series machine. MLC transmission factor from C-Series machine varies between 1.5 % and 1.6 %. Some of the measured data values from C-Series FFF beam are compared with Truebeam representative data. 6MV-FFF beam parameter values like dmax, OP factors, beam symmetry and flatness and additional parameters for C-Series and Truebeam liancs will be presented and compared in graphical form and tabular data form if selected. Conclusion: The 6MV flattening filter (FF) beam data from C-Series & Truebeam and 6MV-FFF beam data from Truebeam has already presented. This particular analysis to compare 6MV-FFF beam from C-Series and Truebeam provides opportunity to better elaborate FFF mode on novel machines. It was found that C-Series and Truebeam 6MV-FFF dosimetric and beam data was quite similar.« less

Geometric dimension model of virtual astronaut body for ergonomic analysis of man-machine space system

NASA Astrophysics Data System (ADS)

Qianxiang, Zhou

2012-07-01

It is very important to clarify the geometric characteristic of human body segment and constitute analysis model for ergonomic design and the application of ergonomic virtual human. The typical anthropometric data of 1122 Chinese men aged 20-35 years were collected using three-dimensional laser scanner for human body. According to the correlation between different parameters, curve fitting were made between seven trunk parameters and ten body parameters with the SPSS 16.0 software. It can be concluded that hip circumference and shoulder breadth are the most important parameters in the models and the two parameters have high correlation with the others parameters of human body. By comparison with the conventional regressive curves, the present regression equation with the seven trunk parameters is more accurate to forecast the geometric dimensions of head, neck, height and the four limbs with high precision. Therefore, it is greatly valuable for ergonomic design and analysis of man-machine system.This result will be very useful to astronaut body model analysis and application.

Precision Parameter Estimation and Machine Learning

NASA Astrophysics Data System (ADS)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang Baolong; Department of Mathematics and Physics, Hefei University, Hefei, 230022; Song Qingming

We present a scheme to realize a special quantum cloning machine in separate cavities. The quantum cloning machine can copy the quantum information from a photon pulse to two distant atoms. Choosing the different parameters, the method can perform optimal symmetric (asymmetric) universal quantum cloning and optimal symmetric (asymmetric) phase-covariant cloning.

Fast machine-learning online optimization of ultra-cold-atom experiments.

PubMed

Wigley, P B; Everitt, P J; van den Hengel, A; Bastian, J W; Sooriyabandara, M A; McDonald, G D; Hardman, K S; Quinlivan, C D; Manju, P; Kuhn, C C N; Petersen, I R; Luiten, A N; Hope, J J; Robins, N P; Hush, M R

2016-05-16

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

Fast machine-learning online optimization of ultra-cold-atom experiments

PubMed Central

Wigley, P. B.; Everitt, P. J.; van den Hengel, A.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C. N.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R.

2016-01-01

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. PMID:27180805

Optimization of cutting parameters in CNC turning of stainless steel 304 with TiAlN nano coated carbide cutting tool

NASA Astrophysics Data System (ADS)

Durga Prasada Rao, V.; Harsha, N.; Raghu Ram, N. S.; Navya Geethika, V.

2018-02-01

In this work, turning was performed to optimize the surface finish or roughness (Ra) of stainless steel 304 with uncoated and coated carbide tools under dry conditions. The carbide tools were coated with Titanium Aluminium Nitride (TiAlN) nano coating using Physical Vapour Deposition (PVD) method. The machining parameters, viz., cutting speed, depth of cut and feed rate which show major impact on Ra are considered during turning. The experiments are designed as per Taguchi orthogonal array and machining process is done accordingly. Then second-order regression equations have been developed on the basis of experimental results for Ra in terms of machining parameters used. Regarding the effect of machining parameters, an upward trend is observed in Ra with respect to feed rate, and as cutting speed increases the Ra value increased slightly due to chatter and vibrations. The adequacy of response variable (Ra) is tested by conducting additional experiments. The predicted Ra values are found to be a close match of their corresponding experimental values of uncoated and coated tools. The corresponding average % errors are found to be within the acceptable limits. Then the surface roughness equations of uncoated and coated tools are set as the objectives of optimization problem and are solved by using Differential Evolution (DE) algorithm. Also the tool lives of uncoated and coated tools are predicted by using Taylor’s tool life equation.

Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach.

PubMed

Yu, Tianwei; Jones, Dean P

2014-10-15

Peak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics. Here we present a method that learns directly from various data features of the extracted ion chromatograms (EICs) to differentiate between true peak regions from noise regions in the LC/MS profile. It utilizes the knowledge of known metabolites, as well as robust machine learning approaches. Unlike currently available methods, this new approach does not assume a parametric peak shape model and allows maximum flexibility. We demonstrate the superiority of the new approach using real data. Because matching to known metabolites entails uncertainties and cannot be considered a gold standard, we also developed a probabilistic receiver-operating characteristic (pROC) approach that can incorporate uncertainties. The new peak detection approach is implemented as part of the apLCMS package available at http://web1.sph.emory.edu/apLCMS/ CONTACT: tyu8@emory.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A single-phase axially-magnetized permanent-magnet oscillating machine for miniature aerospace power sources

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Cheng, Luming; Wang, Weinan; Liu, Jiaqi

2017-05-01

A single-phase axially-magnetized permanent-magnet (PM) oscillating machine which can be integrated with a free-piston Stirling engine to generate electric power, is investigated for miniature aerospace power sources. Machine structure, operating principle and detent force characteristic are elaborately studied. With the sinusoidal speed characteristic of the mover considered, the proposed machine is designed by 2D finite-element analysis (FEA), and some main structural parameters such as air gap diameter, dimensions of PMs, pole pitches of both stator and mover, and the pole-pitch combinations, etc., are optimized to improve both the power density and force capability. Compared with the three-phase PM linear machines, the proposed single-phase machine features less PM use, simple control and low controller cost. The power density of the proposed machine is higher than that of the three-phase radially-magnetized PM linear machine, but lower than the three-phase axially-magnetized PM linear machine.

Gradient Evolution-based Support Vector Machine Algorithm for Classification

NASA Astrophysics Data System (ADS)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

Control system health test system and method

DOEpatents

Hoff, Brian D.; Johnson, Kris W.; Akasam, Sivaprasad; Baker, Thomas M.

2006-08-15

A method is provided for testing multiple elements of a work machine, including a control system, a component, a sub-component that is influenced by operations of the component, and a sensor that monitors a characteristic of the sub-component. In one embodiment, the method is performed by the control system and includes sending a command to the component to adjust a first parameter associated with an operation of the component. Also, the method includes detecting a sensor signal from the sensor reflecting a second parameter associated with a characteristic of the sub-component and determining whether the second parameter is acceptable based on the command. The control system may diagnose at least one of the elements of the work machine when the second parameter of the sub-component is not acceptable.

75 FR 48955 - Arbitration Panel Decision Under the Randolph-Sheppard Act

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-12

... vending machine facility operated by a blind vendor at the USPS's Chicago Processing and Distribution... cafeteria operations are exempt from the Act and whether the vending machines operated by a private vendor at the Chicago Processing and Distribution Center are in direct competition with the vending machines...

Using Neural Networks to Classify Digitized Images of Galaxies

NASA Astrophysics Data System (ADS)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

A general electromagnetic excitation model for electrical machines considering the magnetic saturation and rub impact

NASA Astrophysics Data System (ADS)

Xu, Xueping; Han, Qinkai; Chu, Fulei

2018-03-01

The electromagnetic vibration of electrical machines with an eccentric rotor has been extensively investigated. However, magnetic saturation was often neglected. Moreover, the rub impact between the rotor and stator is inevitable when the amplitude of the rotor vibration exceeds the air-gap. This paper aims to propose a general electromagnetic excitation model for electrical machines. First, a general model which takes the magnetic saturation and rub impact into consideration is proposed and validated by the finite element method and reference. The dynamic equations of a Jeffcott rotor system with electromagnetic excitation and mass imbalance are presented. Then, the effects of pole-pair number and rubbing parameters on vibration amplitude are studied and approaches restraining the amplitude are put forward. Finally, the influences of mass eccentricity, resultant magnetomotive force (MMF), stiffness coefficient, damping coefficient, contact stiffness and friction coefficient on the stability of the rotor system are investigated through the Floquet theory, respectively. The amplitude jumping phenomenon is observed in a synchronous generator for different pole-pair numbers. The changes of design parameters can alter the stability states of the rotor system and the range of parameter values forms the zone of stability, which lays helpful suggestions for the design and application of the electrical machines.

Study of hole characteristics in Laser Trepan Drilling of ZTA

NASA Astrophysics Data System (ADS)

Saini, Surendra K.; Dubey, Avanish K.; Upadhyay, B. N.; Choubey, A.

2018-07-01

Zirconia Toughened Alumina ceramic is widely used for aerospace components, combustion chambers, heat exchangers, bearings and pumps mainly due to its improved mechanical and thermal properties. To make holes in thick section Zirconia Toughened Alumina ceramics is a major challenge due to its unfavorable machining characteristics. Recent researches have explored that laser machining can overcome the machining limitations of advanced materials having improved mechanical properties. In present research, authors have analyzed the effect of Laser Trepan Drilling on hole characteristics of 6.0 mm thick Zirconia Toughened Alumina. Effect of significant process parameters on hole characteristics such as hole circularity at top and bottom, hole taper, and spatter size have been studied. The optimum ranges of these parameters have been suggested on the basis of empirical modeling and optimization.

Variable geometry Darrieus wind machine

NASA Astrophysics Data System (ADS)

Pytlinski, J. T.; Serrano, D.

1983-08-01

A variable geometry Darrieus wind machine is proposed. The lower attachment of the blades to the rotor can move freely up and down the axle allowing the blades of change shape during rotation. Experimental data for a 17 m. diameter Darrieus rotor and a theoretical model for multiple streamtube performance prediction were used to develop a computer simulation program for studying parameters that affect the machine's performance. This new variable geometry concept is described and interrelated with multiple streamtube theory through aerodynamic parameters. The computer simulation study shows that governor behavior of a Darrieus turbine can not be attained by a standard turbine operating within normally occurring rotational velocity limits. A second generation variable geometry Darrieus wind turbine which uses a telescopic blade is proposed as a potential improvement on the studied concept.

Assessing the effects of different dielectrics on environmentally conscious powder-mixed EDM of difficult-to-machine material (WC-Co)

NASA Astrophysics Data System (ADS)

Singh, Jagdeep; Sharma, Rajiv Kumar

2016-12-01

Electrical discharge machining (EDM) is a well-known nontraditional manufacturing process to machine the difficult-to-machine (DTM) materials which have unique hardness properties. Researchers have successfully performed hybridization to improve this process by incorporating powders into the EDM process known as powder-mixed EDM process. This process drastically improves process efficiency by increasing material removal rate, micro-hardness, as well as reducing the tool wear rate and surface roughness. EDM also has some input parameters, including pulse-on time, dielectric levels and its type, current setting, flushing pressure, and so on, which have a significant effect on EDM performance. However, despite their positive influence, investigating the effects of these parameters on environmental conditions is necessary. Most studies demonstrate the use of kerosene oil as dielectric fluid. Nevertheless, in this work, the authors highlight the findings with respect to three different dielectric fluids, including kerosene oil, EDM oil, and distilled water using one-variable-at-a-time approach for machining as well as environmental aspects. The hazard and operability analysis is employed to identify the inherent safety factors associated with powder-mixed EDM of WC-Co.

Research on torsional vibration modelling and control of printing cylinder based on particle swarm optimization

NASA Astrophysics Data System (ADS)

Wang, Y. M.; Xu, W. C.; Wu, S. Q.; Chai, C. W.; Liu, X.; Wang, S. H.

2018-03-01

The torsional oscillation is the dominant vibration form for the impression cylinder of printing machine (printing cylinder for short), directly restricting the printing speed up and reducing the quality of the prints. In order to reduce torsional vibration, the active control method for the printing cylinder is obtained. Taking the excitation force and moment from the cylinder gap and gripper teeth open & closing cam mechanism as variable parameters, authors establish the dynamic mathematical model of torsional vibration for the printing cylinder. The torsional active control method is based on Particle Swarm Optimization(PSO) algorithm to optimize input parameters for the serve motor. Furthermore, the input torque of the printing cylinder is optimized, and then compared with the numerical simulation results. The conclusions are that torsional vibration active control based on PSO is an availability method to the torsional vibration of printing cylinder.

Full-field local displacement analysis of two-sided paperboard

Treesearch

J.M. Considine; D.W. Vahey

2007-01-01

This report describes a method to examine full-field displacements of both sides of paperboard during tensile testing. Analysis showed out-of-plane shear behavior near the failures zones. The method was reliably used to examine out-of-plane shear in double notch shear specimens. Differences in shear behavior of machine direction and cross-machine direction specimens...

From design to manufacturing of asymmetric teeth gears using computer application

NASA Astrophysics Data System (ADS)

Suciu, F.; Dascalescu, A.; Ungureanu, M.

2017-05-01

The asymmetric cylindrical gears, with involutes teeth profiles having different base circle diameters, are nonstandard gears, used with the aim to obtain better function parameters for the active profile. We will expect that the manufacturing of these gears became possible only after the design and realization of some specific tools. The paper present how the computer aided design and applications developed in MATLAB, for obtain the geometrical parameters, in the same time for calculation some functional parameters like stress and displacements, transmission error, efficiency of the gears and the 2D models, generated with AUTOLISP applications, are used for computer aided manufacturing of asymmetric gears with standard tools. So the specific tools considered one of the disadvantages of these gears are not necessary and implicitly the expected supplementary costs are reduced. The calculus algorithm established for the asymmetric gear design application use the „direct design“ of the spur gears. This method offers the possibility of determining first the parameters of the gears, followed by the determination of the asymmetric gear rack’s parameters, based on those of the gears. Using original design method and computer applications have been determined the geometrical parameters, the 2D and 3D models of the asymmetric gears and on the base of these models have been manufacturing on CNC machine tool asymmetric gears.

Critical Speed of The Glass Glue Machine's Creep and Influence Factors Analysis

NASA Astrophysics Data System (ADS)

Yang, Jianxi; Huang, Jian; Wang, Liying; Shi, Jintai

When automatic glass glue machine works, two questions of the machine starting vibrating and stick-slip motion are existing. These problems should be solved. According to these questions, a glue machine's model for studying stick-slip is established. Based on the dynamics system describing of the model, mathematical expression is presented. The creep critical speed expression is constructed referring to existing research achievement and a new conclusion is found. The influencing factors of stiffness, dampness, mass, velocity, difference of static and kinetic coefficient of friction are analyzed through Matlab simulation. Research shows that reasonable choice of influence parameters can improve the creep phenomenon. These all supply the theory evidence for improving the machine's motion stability.

Characterization and Effects of Fiber Pull-Outs in Hole Quality of Carbon Fiber Reinforced Plastics Composite.

PubMed

Alizadeh Ashrafi, Sina; Miller, Peter W; Wandro, Kevin M; Kim, Dave

2016-10-13

Hole quality plays a crucial role in the production of close-tolerance holes utilized in aircraft assembly. Through drilling experiments of carbon fiber-reinforced plastic composites (CFRP), this study investigates the impact of varying drilling feed and speed conditions on fiber pull-out geometries and resulting hole quality parameters. For this study, hole quality parameters include hole size variance, hole roundness, and surface roughness. Fiber pull-out geometries are quantified by using scanning electron microscope (SEM) images of the mechanically-sectioned CFRP-machined holes, to measure pull-out length and depth. Fiber pull-out geometries and the hole quality parameter results are dependent on the drilling feed and spindle speed condition, which determines the forces and undeformed chip thickness during the process. Fiber pull-out geometries influence surface roughness parameters from a surface profilometer, while their effect on other hole quality parameters obtained from a coordinate measuring machine is minimal.

Experimental Research and Mathematical Modeling of Parameters Effecting on Cutting Force and SurfaceRoughness in CNC Turning Process

NASA Astrophysics Data System (ADS)

Zeqiri, F.; Alkan, M.; Kaya, B.; Toros, S.

2018-01-01

In this paper, the effects of cutting parameters on cutting forces and surface roughness based on Taguchi experimental design method are determined. Taguchi L9 orthogonal array is used to investigate the effects of machining parameters. Optimal cutting conditions are determined using the signal/noise (S/N) ratio which is calculated by average surface roughness and cutting force. Using results of analysis, effects of parameters on both average surface roughness and cutting forces are calculated on Minitab 17 using ANOVA method. The material that was investigated is Inconel 625 steel for two cases with heat treatment and without heat treatment. The predicted and calculated values with measurement are very close to each other. Confirmation test of results showed that the Taguchi method was very successful in the optimization of machining parameters for maximum surface roughness and cutting forces in the CNC turning process.

Deviation Value for Conventional X-ray in Hospitals in South Sulawesi Province from 2014 to 2016

NASA Astrophysics Data System (ADS)

Bachtiar, Ilham; Abdullah, Bualkar; Tahir, Dahlan

2018-03-01

This paper describes the conventional X-ray machine parameters tested in the region of South Sulawesi from 2014 to 2016. The objective of this research is to know deviation of every parameter of conventional X-ray machine. The testing parameters were analyzed by using quantitative methods with participatory observational approach. Data collection was performed by testing the output of conventional X-ray plane using non-invasive x-ray multimeter. The test parameters include tube voltage (kV) accuracy, radiation output linearity, reproducibility and radiation beam value (HVL) quality. The results of the analysis show four conventional X-ray test parameters have varying deviation spans, where the tube voltage (kV) accuracy has an average value of 4.12%, the average radiation output linearity is 4.47% of the average reproducibility of 0.62% and the averaged of the radiation beam (HVL) is 3.00 mm.

Application of design sensitivity analysis for greater improvement on machine structural dynamics

NASA Technical Reports Server (NTRS)

Yoshimura, Masataka

1987-01-01

Methodologies are presented for greatly improving machine structural dynamics by using design sensitivity analyses and evaluative parameters. First, design sensitivity coefficients and evaluative parameters of structural dynamics are described. Next, the relations between the design sensitivity coefficients and the evaluative parameters are clarified. Then, design improvement procedures of structural dynamics are proposed for the following three cases: (1) addition of elastic structural members, (2) addition of mass elements, and (3) substantial charges of joint design variables. Cases (1) and (2) correspond to the changes of the initial framework or configuration, and (3) corresponds to the alteration of poor initial design variables. Finally, numerical examples are given for demonstrating the availability of the methods proposed.

Pattern-Based Inverse Modeling for Characterization of Subsurface Flow Models with Complex Geologic Heterogeneity

NASA Astrophysics Data System (ADS)

Golmohammadi, A.; Jafarpour, B.; M Khaninezhad, M. R.

2017-12-01

Calibration of heterogeneous subsurface flow models leads to ill-posed nonlinear inverse problems, where too many unknown parameters are estimated from limited response measurements. When the underlying parameters form complex (non-Gaussian) structured spatial connectivity patterns, classical variogram-based geostatistical techniques cannot describe the underlying connectivity patterns. Modern pattern-based geostatistical methods that incorporate higher-order spatial statistics are more suitable for describing such complex spatial patterns. Moreover, when the underlying unknown parameters are discrete (geologic facies distribution), conventional model calibration techniques that are designed for continuous parameters cannot be applied directly. In this paper, we introduce a novel pattern-based model calibration method to reconstruct discrete and spatially complex facies distributions from dynamic flow response data. To reproduce complex connectivity patterns during model calibration, we impose a feasibility constraint to ensure that the solution follows the expected higher-order spatial statistics. For model calibration, we adopt a regularized least-squares formulation, involving data mismatch, pattern connectivity, and feasibility constraint terms. Using an alternating directions optimization algorithm, the regularized objective function is divided into a continuous model calibration problem, followed by mapping the solution onto the feasible set. The feasibility constraint to honor the expected spatial statistics is implemented using a supervised machine learning algorithm. The two steps of the model calibration formulation are repeated until the convergence criterion is met. Several numerical examples are used to evaluate the performance of the developed method.

Accurate Micro-Tool Manufacturing by Iterative Pulsed-Laser Ablation

NASA Astrophysics Data System (ADS)

Warhanek, Maximilian; Mayr, Josef; Dörig, Christian; Wegener, Konrad

2017-12-01

Iterative processing solutions, including multiple cycles of material removal and measurement, are capable of achieving higher geometric accuracy by compensating for most deviations manifesting directly on the workpiece. Remaining error sources are the measurement uncertainty and the repeatability of the material-removal process including clamping errors. Due to the lack of processing forces, process fluids and wear, pulsed-laser ablation has proven high repeatability and can be realized directly on a measuring machine. This work takes advantage of this possibility by implementing an iterative, laser-based correction process for profile deviations registered directly on an optical measurement machine. This way efficient iterative processing is enabled, which is precise, applicable for all tool materials including diamond and eliminates clamping errors. The concept is proven by a prototypical implementation on an industrial tool measurement machine and a nanosecond fibre laser. A number of measurements are performed on both the machine and the processed workpieces. Results show production deviations within 2 μm diameter tolerance.

Identifying product order with restricted Boltzmann machines

NASA Astrophysics Data System (ADS)

Rao, Wen-Jia; Li, Zhenyu; Zhu, Qiong; Luo, Mingxing; Wan, Xin

2018-03-01

Unsupervised machine learning via a restricted Boltzmann machine is a useful tool in distinguishing an ordered phase from a disordered phase. Here we study its application on the two-dimensional Ashkin-Teller model, which features a partially ordered product phase. We train the neural network with spin configuration data generated by Monte Carlo simulations and show that distinct features of the product phase can be learned from nonergodic samples resulting from symmetry breaking. Careful analysis of the weight matrices inspires us to define a nontrivial machine-learning motivated quantity of the product form, which resembles the conventional product order parameter.

Chip formation and surface integrity in high-speed machining of hardened steel

NASA Astrophysics Data System (ADS)

Kishawy, Hossam Eldeen A.

Increasing demands for high production rates as well as cost reduction have emphasized the potential for the industrial application of hard turning technology during the past few years. Machining instead of grinding hardened steel components reduces the machining sequence, the machining time, and the specific cutting energy. Hard turning Is characterized by the generation of high temperatures, the formation of saw toothed chips, and the high ratio of thrust to tangential cutting force components. Although a large volume of literature exists on hard turning, the change in machined surface physical properties represents a major challenge. Thus, a better understanding of the cutting mechanism in hard turning is still required. In particular, the chip formation process and the surface integrity of the machined surface are important issues which require further research. In this thesis, a mechanistic model for saw toothed chip formation is presented. This model is based on the concept of crack initiation on the free surface of the workpiece. The model presented explains the mechanism of chip formation. In addition, experimental investigation is conducted in order to study the chip morphology. The effect of process parameters, including edge preparation and tool wear on the chip morphology, is studied using Scanning Electron Microscopy (SEM). The dynamics of chip formation are also investigated. The surface integrity of the machined parts is also investigated. This investigation focusses on residual stresses as well as surface and sub-surface deformation. A three dimensional thermo-elasto-plastic finite element model is developed to predict the machining residual stresses. The effect of flank wear is introduced during the analysis. Although residual stresses have complicated origins and are introduced by many factors, in this model only the thermal and mechanical factors are considered. The finite element analysis demonstrates the significant effect of the heat generated during cutting on the residual stresses. The machined specimens are also examined using x-ray diffraction technique to clarify the effect of different speeds, feeds and depths of cut as well as different edge preparations on the residual stress distribution beneath the machined surface. A reasonable agreement between the predicted and measured residual stress is obtained. The results obtained demonstrate the possibility of eliminating the existence of high tensile residual stresses in the workpiece surface by selecting the proper cutting conditions. The machined surfaces are examined using SEM to study the effect of different process parameters and edge preparations on the quality of the machined surface. The phenomenon of material side flow is investigated to clarify the mechanism of this phenomenon. The effect of process parameters and edge preparations on sub-surface deformation is also investigated.

Function library programming to support B89 evaluation of Sheffield Apollo RS50 DCC (Direct Computer Control) CMM (Coordinate Measuring Machine)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, R.N.

1990-02-28

The Inspection Shop at Lawrence Livermore Lab recently purchased a Sheffield Apollo RS50 Direct Computer Control Coordinate Measuring Machine. The performance of the machine was specified to conform to B89 standard which relies heavily upon using the measuring machine in its intended manner to verify its accuracy (rather than parametric tests). Although it would be possible to use the interactive measurement system to perform these tasks, a more thorough and efficient job can be done by creating Function Library programs for certain tasks which integrate Hewlett-Packard Basic 5.0 language and calls to proprietary analysis and machine control routines. This combinationmore » provides efficient use of the measuring machine with a minimum of keyboard input plus an analysis of the data with respect to the B89 Standard rather than a CMM analysis which would require subsequent interpretation. This paper discusses some characteristics of the Sheffield machine control and analysis software and my use of H-P Basic language to create automated measurement programs to support the B89 performance evaluation of the CMM. 1 ref.« less

Do capuchin monkeys (Cebus apella) diagnose causal relations in the absence of a direct reward?

PubMed

Edwards, Brian J; Rottman, Benjamin M; Shankar, Maya; Betzler, Riana; Chituc, Vladimir; Rodriguez, Ricardo; Silva, Liara; Wibecan, Leah; Widness, Jane; Santos, Laurie R

2014-01-01

We adapted a method from developmental psychology to explore whether capuchin monkeys (Cebus apella) would place objects on a "blicket detector" machine to diagnose causal relations in the absence of a direct reward. Across five experiments, monkeys could place different objects on the machine and obtain evidence about the objects' causal properties based on whether each object "activated" the machine. In Experiments 1-3, monkeys received both audiovisual cues and a food reward whenever the machine activated. In these experiments, monkeys spontaneously placed objects on the machine and succeeded at discriminating various patterns of statistical evidence. In Experiments 4 and 5, we modified the procedure so that in the learning trials, monkeys received the audiovisual cues when the machine activated, but did not receive a food reward. In these experiments, monkeys failed to test novel objects in the absence of an immediate food reward, even when doing so could provide critical information about how to obtain a reward in future test trials in which the food reward delivery device was reattached. The present studies suggest that the gap between human and animal causal cognition may be in part a gap of motivation. Specifically, we propose that monkey causal learning is motivated by the desire to obtain a direct reward, and that unlike humans, monkeys do not engage in learning for learning's sake.

Do Capuchin Monkeys (Cebus apella) Diagnose Causal Relations in the Absence of a Direct Reward?

PubMed Central

Edwards, Brian J.; Rottman, Benjamin M.; Shankar, Maya; Betzler, Riana; Chituc, Vladimir; Rodriguez, Ricardo; Silva, Liara; Wibecan, Leah; Widness, Jane; Santos, Laurie R.

2014-01-01

We adapted a method from developmental psychology [1] to explore whether capuchin monkeys (Cebus apella) would place objects on a “blicket detector” machine to diagnose causal relations in the absence of a direct reward. Across five experiments, monkeys could place different objects on the machine and obtain evidence about the objects’ causal properties based on whether each object “activated” the machine. In Experiments 1–3, monkeys received both audiovisual cues and a food reward whenever the machine activated. In these experiments, monkeys spontaneously placed objects on the machine and succeeded at discriminating various patterns of statistical evidence. In Experiments 4 and 5, we modified the procedure so that in the learning trials, monkeys received the audiovisual cues when the machine activated, but did not receive a food reward. In these experiments, monkeys failed to test novel objects in the absence of an immediate food reward, even when doing so could provide critical information about how to obtain a reward in future test trials in which the food reward delivery device was reattached. The present studies suggest that the gap between human and animal causal cognition may be in part a gap of motivation. Specifically, we propose that monkey causal learning is motivated by the desire to obtain a direct reward, and that unlike humans, monkeys do not engage in learning for learning’s sake. PMID:24586347

An iterative learning control method with application for CNC machine tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

A Comprehensive Review of Permanent Magnet Transverse Flux Machines for Direct Drive Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muljadi, Eduard; Husain, Tausif; Hasan, Iftekhar

The use of direct drive machines in renewable and industrial applications are increasing at a rapid rate. Transverse flux machines (TFM) are ideally suited for direct drive applications due to their high torque density. In this paper, a comprehensive review of the permanent magnet (PM) TFMs for direct drive applications is presented. The paper introduces TFMs and their operating principle and then reviews the different type of TFMs proposed in the literature. The TFMs are categorized according to the number of stator sides, types of stator cores and magnet arrangement in the rotor. The review covers different design topologies, materialsmore » used for manufacturing, structural and thermal analysis, modeling and design optimization and cogging torque minimization in TFMs. The paper also reviews various applications and comparisons for TFMs that have been presented in the literature.« less

Modelling of Mechanical Behavior at High Strain Rate of Ti-6al-4v Manufactured By Means of Direct Metal Laser Sintering Technique

NASA Astrophysics Data System (ADS)

Iannitti, Gianluca; Bonora, Nicola; Gentile, Domenico; Ruggiero, Andrew; Testa, Gabriel; Gubbioni, Simone

2017-06-01

In this work, the mechanical behavior of Ti-6Al-4V obtained by additive manufacturing technique was investigated, also considering the build direction. Dog-bone shaped specimens and Taylor cylinders were machined from rods manufactured by means of the EOSSINT M2 80 machine, based on Direct Metal Laser Sintering technique. Tensile tests were performed at strain rate ranging from 5E-4 s-1 to 1000 s-1 using an Instron electromechanical machine for quasistatic tests and a Direct-Tension Split Hopkinson Bar for dynamic tests. The mechanical strength of the material was described by a Johnson-Cook model modified to account for stress saturation occurring at high strain. Taylor cylinder tests and their corresponding numerical simulations were carried out in order to validate the constitutive model under a complex deformation path, high strain rates, and high temperatures.

Integrated modeling and analysis of the multiple electromechanical couplings for the direct driven feed system in machine tools

NASA Astrophysics Data System (ADS)

Yang, Xiaojun; Lu, Dun; Liu, Hui; Zhao, Wanhua

2018-06-01

The complicated electromechanical coupling phenomena due to different kinds of causes have significant influences on the dynamic precision of the direct driven feed system in machine tools. In this paper, a novel integrated modeling and analysis method of the multiple electromechanical couplings for the direct driven feed system in machine tools is presented. At first, four different kinds of electromechanical coupling phenomena in the direct driven feed system are analyzed systematically. Then a novel integrated modeling and analysis method of the electromechanical coupling which is influenced by multiple factors is put forward. In addition, the effects of multiple electromechanical couplings on the dynamic precision of the feed system and their main influencing factors are compared and discussed, respectively. Finally, the results of modeling and analysis are verified by the experiments. It finds out that multiple electromechanical coupling loops, which are overlapped and influenced by each other, are the main reasons of the displacement fluctuations in the direct driven feed system.

Determination of Machining Parameters of Corn Byproduct Filled Plastics

USDA-ARS?s Scientific Manuscript database

In a collaborative project between the USDA and Northern Illinois University, the use of ethanol corn processing by-products as bio-filler materials in the compression molding of phenolic plastics has been studied. This paper reports on the results of a machinability study in the milling of various ...

Determining Machining Parameters of Corn Byproduct Filled Plastics

USDA-ARS?s Scientific Manuscript database

In a collaborative project between the USDA and Northern Illinois University, the use of corn ethanol processing byproducts (i.e., DDGS) as bio-filler materials in the compression molding of phenolic plastics has been studied. This paper reports on the results of a machinability study in the milling...

Temperature Measurement and Numerical Prediction in Machining Inconel 718

PubMed Central

Tapetado, Alberto; Vázquez, Carmen; Miguélez, Henar

2017-01-01

Thermal issues are critical when machining Ni-based superalloy components designed for high temperature applications. The low thermal conductivity and extreme strain hardening of this family of materials results in elevated temperatures around the cutting area. This elevated temperature could lead to machining-induced damage such as phase changes and residual stresses, resulting in reduced service life of the component. Measurement of temperature during machining is crucial in order to control the cutting process, avoiding workpiece damage. On the other hand, the development of predictive tools based on numerical models helps in the definition of machining processes and the obtainment of difficult to measure parameters such as the penetration of the heated layer. However, the validation of numerical models strongly depends on the accurate measurement of physical parameters such as temperature, ensuring the calibration of the model. This paper focuses on the measurement and prediction of temperature during the machining of Ni-based superalloys. The temperature sensor was based on a fiber-optic two-color pyrometer developed for localized temperature measurements in turning of Inconel 718. The sensor is capable of measuring temperature in the range of 250 to 1200 °C. Temperature evolution is recorded in a lathe at different feed rates and cutting speeds. Measurements were used to calibrate a simplified numerical model for prediction of temperature fields during turning. PMID:28665312

Application of target costing in machining

NASA Astrophysics Data System (ADS)

Gopalakrishnan, Bhaskaran; Kokatnur, Ameet; Gupta, Deepak P.

2004-11-01

In today's intensely competitive and highly volatile business environment, consistent development of low cost and high quality products meeting the functionality requirements is a key to a company's survival. Companies continuously strive to reduce the costs while still producing quality products to stay ahead in the competition. Many companies have turned to target costing to achieve this objective. Target costing is a structured approach to determine the cost at which a proposed product, meeting the quality and functionality requirements, must be produced in order to generate the desired profits. It subtracts the desired profit margin from the company's selling price to establish the manufacturing cost of the product. Extensive literature review revealed that companies in automotive, electronic and process industries have reaped the benefits of target costing. However target costing approach has not been applied in the machining industry, but other techniques based on Geometric Programming, Goal Programming, and Lagrange Multiplier have been proposed for application in this industry. These models follow a forward approach, by first selecting a set of machining parameters, and then determining the machining cost. Hence in this study we have developed an algorithm to apply the concepts of target costing, which is a backward approach that selects the machining parameters based on the required machining costs, and is therefore more suitable for practical applications in process improvement and cost reduction. A target costing model was developed for turning operation and was successfully validated using practical data.

Pulsed, Hydraulic Coal-Mining Machine

NASA Technical Reports Server (NTRS)

Collins, Earl R., Jr.

1986-01-01

In proposed coal-cutting machine, piston forces water through nozzle, expelling pulsed jet that cuts into coal face. Spring-loaded piston reciprocates at end of travel to refill water chamber. Machine a onecylinder, two-cycle, internal-combustion engine, fueled by gasoline, diesel fuel, or hydrogen. Fuel converted more directly into mechanical energy of water jet.

The Machines Are Coming: Future Directions in Instructional Communication Research. Forum: The Future of Instructional Communication

ERIC Educational Resources Information Center

Edwards, Autumn; Edwards, Chad

2017-01-01

Educational encounters of the future (and increasingly, of the present) will involve a complex collaboration of human and machine intelligences and agents, partnering to enhance learning and growth. Increasingly, "students and instructors are not only talking 'through' machines, but also [talking] 'to them', and 'within them'" (Edwards…

Bypassing the Kohn-Sham equations with machine learning.

PubMed

Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E; Burke, Kieron; Müller, Klaus-Robert

2017-10-11

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Machine Learning Force Field Parameters from Ab Initio Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Ying; Li, Hui; Pickard, Frank C.

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor duringmore » the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.« less

High pressure water jet mining machine

DOEpatents

Barker, Clark R.

1981-05-05

A high pressure water jet mining machine for the longwall mining of coal is described. The machine is generally in the shape of a plowshare and is advanced in the direction in which the coal is cut. The machine has mounted thereon a plurality of nozzle modules each containing a high pressure water jet nozzle disposed to oscillate in a particular plane. The nozzle modules are oriented to cut in vertical and horizontal planes on the leading edge of the machine and the coal so cut is cleaved off by the wedge-shaped body.

Inference of Surface Parameters from Near-Infrared Spectra of Crystalline H2O Ice with Neural Learning

NASA Astrophysics Data System (ADS)

Zhang, Lili; Merényi, Erzsébet; Grundy, William M.; Young, Eliot F.

2010-07-01

The near-infrared spectra of icy volatiles collected from planetary surfaces can be used to infer surface parameters, which in turn may depend on the recent geologic history. The high dimensionality and complexity of the spectral data, the subtle differences between the spectra, and the highly nonlinear interplay between surface parameters make it often difficult to accurately derive these surface parameters. We use a neural machine, with a Self-Organizing Map (SOM) as its hidden layer, to infer the latent physical parameters, temperature and grain size from near-infrared spectra of crystalline H2O ice. The output layer of the SOM-hybrid machine is customarily trained with only the output from the SOM winner. We show that this scheme prevents simultaneous achievement of high prediction accuracies for both parameters. We propose an innovative neural architecture we call Conjoined Twins that allows multiple (k) SOM winners to participate in the training of the output layer and in which the customization of k can be limited automatically to a small range. With this novel machine we achieve scientifically useful accuracies, 83.0 ± 2.7% and 100.0 ± 0.0%, for temperature and grain size, respectively, from simulated noiseless spectra. We also show that the performance of the neural model is robust under various noisy conditions. A primary application of this prediction capability is planned for spectra returned from the Pluto-Charon system by New Horizons.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Direct Machining of Low-Loss THz Waveguide Components With an RF Choke.

PubMed

Lewis, Samantha M; Nanni, Emilio A; Temkin, Richard J

2014-12-01

We present results for the successful fabrication of low-loss THz metallic waveguide components using direct machining with a CNC end mill. The approach uses a split-block machining process with the addition of an RF choke running parallel to the waveguide. The choke greatly reduces coupling to the parasitic mode of the parallel-plate waveguide produced by the split-block. This method has demonstrated loss as low as 0.2 dB/cm at 280 GHz for a copper WR-3 waveguide. It has also been used in the fabrication of 3 and 10 dB directional couplers in brass, demonstrating excellent agreement with design simulations from 240-260 GHz. The method may be adapted to structures with features on the order of 200 μm.

Ring Image Analyzer

NASA Technical Reports Server (NTRS)

Strekalov, Dmitry V.

2012-01-01

Ring Image Analyzer software analyzes images to recognize elliptical patterns. It determines the ellipse parameters (axes ratio, centroid coordinate, tilt angle). The program attempts to recognize elliptical fringes (e.g., Newton Rings) on a photograph and determine their centroid position, the short-to-long-axis ratio, and the angle of rotation of the long axis relative to the horizontal direction on the photograph. These capabilities are important in interferometric imaging and control of surfaces. In particular, this program has been developed and applied for determining the rim shape of precision-machined optical whispering gallery mode resonators. The program relies on a unique image recognition algorithm aimed at recognizing elliptical shapes, but can be easily adapted to other geometric shapes. It is robust against non-elliptical details of the image and against noise. Interferometric analysis of precision-machined surfaces remains an important technological instrument in hardware development and quality analysis. This software automates and increases the accuracy of this technique. The software has been developed for the needs of an R&TD-funded project and has become an important asset for the future research proposal to NASA as well as other agencies.

Experimental study of surface integrity and fatigue life in the face milling of inconel 718

NASA Astrophysics Data System (ADS)

Wang, Xiangyu; Huang, Chuanzhen; Zou, Bin; Liu, Guoliang; Zhu, Hongtao; Wang, Jun

2018-06-01

The Inconel 718 alloy is widely used in the aerospace and power industries. The machining-induced surface integrity and fatigue life of this material are important factors for consideration due to high reliability and safety requirements. In this work, the milling of Inconel 718 was conducted at different cutting speeds and feed rates. Surface integrity and fatigue life were measured directly. The effects of cutting speed and feed rate on surface integrity and their further influences on fatigue life were analyzed. Within the chosen parameter range, the cutting speed barely affected the surface roughness, whereas the feed rate increased the surface roughness through the ideal residual height. The surface hardness increased as the cutting speed and feed rate increased. Tensile residual stress was observed on the machined surface, which showed improvement with the increasing feed rate. The cutting speed was not an influencing factor on fatigue life, but the feed rate affected fatigue life through the surface roughness. The high surface roughness resulting from the high feed rate could result in a high stress concentration factor and lead to a low fatigue life.

Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

PubMed

Stoica, C; Camejo, J; Banciu, A; Nita-Lazar, M; Paun, I; Cristofor, S; Pacheco, O R; Guevara, M

2016-01-01

Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

Optimization of Dimensional accuracy in plasma arc cutting process employing parametric modelling approach

NASA Astrophysics Data System (ADS)

Naik, Deepak kumar; Maity, K. P.

2018-03-01

Plasma arc cutting (PAC) is a high temperature thermal cutting process employed for the cutting of extensively high strength material which are difficult to cut through any other manufacturing process. This process involves high energized plasma arc to cut any conducting material with better dimensional accuracy in lesser time. This research work presents the effect of process parameter on to the dimensional accuracy of PAC process. The input process parameters were selected as arc voltage, standoff distance and cutting speed. A rectangular plate of 304L stainless steel of 10 mm thickness was taken for the experiment as a workpiece. Stainless steel is very extensively used material in manufacturing industries. Linear dimension were measured following Taguchi’s L16 orthogonal array design approach. Three levels were selected to conduct the experiment for each of the process parameter. In all experiments, clockwise cut direction was followed. The result obtained thorough measurement is further analyzed. Analysis of variance (ANOVA) and Analysis of means (ANOM) were performed to evaluate the effect of each process parameter. ANOVA analysis reveals the effect of input process parameter upon leaner dimension in X axis. The results of the work shows that the optimal setting of process parameter values for the leaner dimension on the X axis. The result of the investigations clearly show that the specific range of input process parameter achieved the improved machinability.

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining

PubMed Central

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-01-01

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing. PMID:27854322

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining.

PubMed

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-11-16

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

Effect of High-speed Milling tool path strategies on the surface roughness of Stavax ESR mold insert machining

NASA Astrophysics Data System (ADS)

Mebrahitom, A.; Rizuan, D.; Azmir, M.; Nassif, M.

2016-02-01

High speed milling is one of the recent technologies used to produce mould inserts due to the need for high surface finish. It is a faster machining process where it uses a small side step and a small down step combined with very high spindle speed and feed rate. In order to effectively use the HSM capabilities, optimizing the tool path strategies and machining parameters is an important issue. In this paper, six different tool path strategies have been investigated on the surface finish and machining time of a rectangular cavities of ESR Stavax material. CAD/CAM application of CATIA V5 machining module for pocket milling of the cavities was used for process planning.

Investigation of approximate models of experimental temperature characteristics of machines

NASA Astrophysics Data System (ADS)

Parfenov, I. V.; Polyakov, A. N.

2018-05-01

This work is devoted to the investigation of various approaches to the approximation of experimental data and the creation of simulation mathematical models of thermal processes in machines with the aim of finding ways to reduce the time of their field tests and reducing the temperature error of the treatments. The main methods of research which the authors used in this work are: the full-scale thermal testing of machines; realization of various approaches at approximation of experimental temperature characteristics of machine tools by polynomial models; analysis and evaluation of modelling results (model quality) of the temperature characteristics of machines and their derivatives up to the third order in time. As a result of the performed researches, rational methods, type, parameters and complexity of simulation mathematical models of thermal processes in machine tools are proposed.

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

Improvement of human operator vibroprotection system in the utility machine

NASA Astrophysics Data System (ADS)

Korchagin, P. A.; Teterina, I. A.; Rahuba, L. F.

2018-01-01

The article is devoted to an urgent problem of improving efficiency of road-building utility machines in terms of improving human operator vibroprotection system by determining acceptable values of the rigidity coefficients and resistance coefficients of operator’s cab suspension system elements and those of operator’s seat. Negative effects of vibration result in labour productivity decrease and occupational diseases. Besides, structure vibrations have a damaging impact on the machine units and mechanisms, which leads to reducing an overall service life of the machine. Results of experimental and theoretical research of operator vibroprotection system in the road-building utility machine are presented. An algorithm for the program to calculate dynamic impacts on the operator in terms of different structural and performance parameters of the machine and considering combination of external pertrubation influences was proposed.

Investigation of a tubular dual-stator flux-switching permanent-magnet linear generator for free-piston energy converter

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Tong, Chengde; Yu, Bin; Zhu, Shaohong; Zhu, Jianguo

2015-05-01

This paper describes a tubular dual-stator flux-switching permanent-magnet (PM) linear generator for free-piston energy converter. The operating principle, topology, and design considerations of the machine are investigated. Combining the motion characteristic of free-piston Stirling engine, a tubular dual-stator PM linear generator is designed by finite element method. Some major structural parameters, such as the outer and inner radii of the mover, PM thickness, mover tooth width, tooth width of the outer and inner stators, etc., are optimized to improve the machine performances like thrust capability and power density. In comparison with conventional single-stator PM machines like moving-magnet linear machine and flux-switching linear machine, the proposed dual-stator flux-switching PM machine shows advantages in higher mass power density, higher volume power density, and lighter mover.

A tubular hybrid Halbach/axially-magnetized permanent-magnet linear machine

NASA Astrophysics Data System (ADS)

Sui, Yi; Liu, Yong; Cheng, Luming; Liu, Jiaqi; Zheng, Ping

2017-05-01

A single-phase tubular permanent-magnet linear machine (PMLM) with hybrid Halbach/axially-magnetized PM arrays is proposed for free-piston Stirling power generation system. Machine topology and operating principle are elaborately illustrated. With the sinusoidal speed characteristic of the free-piston Stirling engine considered, the proposed machine is designed and calculated by finite-element analysis (FEA). The main structural parameters, such as outer radius of the mover, radial length of both the axially-magnetized PMs and ferromagnetic poles, axial length of both the middle and end radially-magnetized PMs, etc., are optimized to improve both the force capability and power density. Compared with the conventional PMLMs, the proposed machine features high mass and volume power density, and has the advantages of simple control and low converter cost. The proposed machine topology is applicable to tubular PMLMs with any phases.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolme, David S; Mikkilineni, Aravind K; Rose, Derek C

Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilizemore » measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.« less

Using Perturbed Physics Ensembles and Machine Learning to Select Parameters for Reducing Regional Biases in a Global Climate Model

NASA Astrophysics Data System (ADS)

Li, S.; Rupp, D. E.; Hawkins, L.; Mote, P.; McNeall, D. J.; Sarah, S.; Wallom, D.; Betts, R. A.

2017-12-01

This study investigates the potential to reduce known summer hot/dry biases over Pacific Northwest in the UK Met Office's atmospheric model (HadAM3P) by simultaneously varying multiple model parameters. The bias-reduction process is done through a series of steps: 1) Generation of perturbed physics ensemble (PPE) through the volunteer computing network weather@home; 2) Using machine learning to train "cheap" and fast statistical emulators of climate model, to rule out regions of parameter spaces that lead to model variants that do not satisfy observational constraints, where the observational constraints (e.g., top-of-atmosphere energy flux, magnitude of annual temperature cycle, summer/winter temperature and precipitation) are introduced sequentially; 3) Designing a new PPE by "pre-filtering" using the emulator results. Steps 1) through 3) are repeated until results are considered to be satisfactory (3 times in our case). The process includes a sensitivity analysis to find dominant parameters for various model output metrics, which reduces the number of parameters to be perturbed with each new PPE. Relative to observational uncertainty, we achieve regional improvements without introducing large biases in other parts of the globe. Our results illustrate the potential of using machine learning to train cheap and fast statistical emulators of climate model, in combination with PPEs in systematic model improvement.

Micro- to Macroroughness of Additively Manufactured Titanium Implants in Terms of Coagulation and Contact Activation.

PubMed

Klingvall Ek, Rebecca; Hong, Jaan; Thor, Andreas; Bäckström, Mikael; Rännar, Lars-Erik

This study aimed to evaluate how as-built electron beam melting (EBM) surface properties affect the onset of blood coagulation. The properties of EBM-manufactured implant surfaces for placement have, until now, remained largely unexplored in literature. Implants with conventional designs and custom-made implants have been manufactured using EBM technology and later placed into the human body. Many of the conventional implants used today, such as dental implants, display modified surfaces to optimize bone ingrowth, whereas custom-made implants, by and large, have machined surfaces. However, titanium in itself demonstrates good material properties for the purpose of bone ingrowth. Specimens manufactured using EBM were selected according to their surface roughness and process parameters. EBM-produced specimens, conventional machined titanium surfaces, as well as PVC surfaces for control were evaluated using the slide chamber model. A significant increase in activation was found, in all factors evaluated, between the machined samples and EBM-manufactured samples. The results show that EBM-manufactured implants with as-built surfaces augment the thrombogenic properties. EBM that uses Ti6Al4V powder appears to be a good manufacturing solution for load-bearing implants with bone anchorage. The as-built surfaces can be used "as is" for direct bone contact, although any surface treatment available for conventional implants can be performed on EBM-manufactured implants with a conventional design.

Fabrication Quality Analysis of a Fiber Optic Refractive Index Sensor Created by CO2 Laser Machining

PubMed Central

Chen, Chien-Hsing; Yeh, Bo-Kuan; Tang, Jaw-Luen; Wu, Wei-Te

2013-01-01

This study investigates the CO2 laser-stripped partial cladding of silica-based optic fibers with a core diameter of 400 μm, which enables them to sense the refractive index of the surrounding environment. However, inappropriate treatments during the machining process can generate a number of defects in the optic fiber sensors. Therefore, the quality of optic fiber sensors fabricated using CO2 laser machining must be analyzed. The results show that analysis of the fiber core size after machining can provide preliminary defect detection, and qualitative analysis of the optical transmission defects can be used to identify imperfections that are difficult to observe through size analysis. To more precisely and quantitatively detect fabrication defects, we included a tensile test and numerical aperture measurements in this study. After a series of quality inspections, we proposed improvements to the existing CO2 laser machining parameters, namely, a vertical scanning pathway, 4 W of power, and a feed rate of 9.45 cm/s. Using these improved parameters, we created optical fiber sensors with a core diameter of approximately 400 μm, no obvious optical transmission defects, a numerical aperture of 0.52 ± 0.019, a 0.886 Weibull modulus, and a 1.186 Weibull-shaped parameter. Finally, we used the optical fiber sensor fabricated using the improved parameters to measure the refractive indices of various solutions. The results show that a refractive-index resolution of 1.8 × 10−4 RIU (linear fitting R2 = 0.954) was achieved for sucrose solutions with refractive indices ranging between 1.333 and 1.383. We also adopted the particle plasmon resonance sensing scheme using the fabricated optical fibers. The results provided additional information, specifically, a superior sensor resolution of 5.73 × 10−5 RIU, and greater linearity at R2 = 0.999. PMID:23535636

Global Seabed Materials and Habitats Mapped: The Computational Methods

NASA Astrophysics Data System (ADS)

Jenkins, C. J.

2016-02-01

What the seabed is made of has proven difficult to map on the scale of whole ocean-basins. Direct sampling and observation can be augmented with proxy-parameter methods such as acoustics. Both avenues are essential to obtain enough detail and coverage, and also to validate the mapping methods. We focus on the direct observations such as samplings, photo and video, probes, diver and sub reports, and surveyed features. These are often in word-descriptive form: over 85% of the records for site materials are in this form, whether as sample/view descriptions or classifications, or described parameters such as consolidation, color, odor, structures and components. Descriptions are absolutely necessary for unusual materials and for processes - in other words, for research. This project dbSEABED not only has the largest collection of seafloor materials data worldwide, but it uses advanced computing math to obtain the best possible coverages and detail. Included in those techniques are linguistic text analysis (e.g., Natural Language Processing, NLP), fuzzy set theory (FST), and machine learning (ML, e.g., Random Forest). These techniques allow efficient and accurate import of huge datasets, thereby optimizing the data that exists. They merge quantitative and qualitative types of data for rich parameter sets, and extrapolate where the data are sparse for best map production. The dbSEABED data resources are now very widely used worldwide in oceanographic research, environmental management, the geosciences, engineering and survey.

Carbon dioxide emission prediction using support vector machine

NASA Astrophysics Data System (ADS)

Saleh, Chairul; Rachman Dzakiyullah, Nur; Bayu Nugroho, Jonathan

2016-02-01

In this paper, the SVM model was proposed for predict expenditure of carbon (CO2) emission. The energy consumption such as electrical energy and burning coal is input variable that affect directly increasing of CO2 emissions were conducted to built the model. Our objective is to monitor the CO2 emission based on the electrical energy and burning coal used from the production process. The data electrical energy and burning coal used were obtained from Alcohol Industry in order to training and testing the models. It divided by cross-validation technique into 90% of training data and 10% of testing data. To find the optimal parameters of SVM model was used the trial and error approach on the experiment by adjusting C parameters and Epsilon. The result shows that the SVM model has an optimal parameter on C parameters 0.1 and 0 Epsilon. To measure the error of the model by using Root Mean Square Error (RMSE) with error value as 0.004. The smallest error of the model represents more accurately prediction. As a practice, this paper was contributing for an executive manager in making the effective decision for the business operation were monitoring expenditure of CO2 emission.

Directional Multi-scale Modeling of High-Resolution Computed Tomography (HRCT) Lung Images for Diffuse Lung Disease Classification

NASA Astrophysics Data System (ADS)

Vo, Kiet T.; Sowmya, Arcot

A directional multi-scale modeling scheme based on wavelet and contourlet transforms is employed to describe HRCT lung image textures for classifying four diffuse lung disease patterns: normal, emphysema, ground glass opacity (GGO) and honey-combing. Generalized Gaussian density parameters are used to represent the detail sub-band features obtained by wavelet and contourlet transforms. In addition, support vector machines (SVMs) with excellent performance in a variety of pattern classification problems are used as classifier. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512x512, 16 bits/pixel in DICOM format. The dataset contains 70,000 ROIs of those slices marked by experienced radiologists. We employ this technique at different wavelet and contourlet transform scales for diffuse lung disease classification. The technique presented here has best overall sensitivity 93.40% and specificity 98.40%.

Optimization and Analysis of Laser Beam Machining Parameters for Al7075-TiB2 In-situ Composite

NASA Astrophysics Data System (ADS)

Manjoth, S.; Keshavamurthy, R.; Pradeep Kumar, G. S.

2016-09-01

The paper focuses on laser beam machining (LBM) of In-situ synthesized Al7075-TiB2 metal matrix composite. Optimization and influence of laser machining process parameters on surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy of composites were studied. Al7075-TiB2 metal matrix composite was synthesized by in-situ reaction technique using stir casting process. Taguchi's L9 orthogonal array was used to design experimental trials. Standoff distance (SOD) (0.3 - 0.5mm), Cutting Speed (1000 - 1200 m/hr) and Gas pressure (0.5 - 0.7 bar) were considered as variable input parameters at three different levels, while power and nozzle diameter were maintained constant with air as assisting gas. Optimized process parameters for surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy were calculated by generating the main effects plot for signal noise ratio (S/N ratio) for surface roughness, VMRR and dimensional error using Minitab software (version 16). The Significant of standoff distance (SOD), cutting speed and gas pressure on surface roughness, volumetric material removal rate (VMRR) and dimensional error were calculated using analysis of variance (ANOVA) method. Results indicate that, for surface roughness, cutting speed (56.38%) is most significant parameter followed by standoff distance (41.03%) and gas pressure (2.6%). For volumetric material removal (VMRR), gas pressure (42.32%) is most significant parameter followed by cutting speed (33.60%) and standoff distance (24.06%). For dimensional error, Standoff distance (53.34%) is most significant parameter followed by cutting speed (34.12%) and gas pressure (12.53%). Further, verification experiments were carried out to confirm performance of optimized process parameters.

A Machine Vision Quality Control System for Industrial Acrylic Fibre Production

NASA Astrophysics Data System (ADS)

Heleno, Paulo; Davies, Roger; Correia, Bento A. Brázio; Dinis, João

2002-12-01

This paper describes the implementation of INFIBRA, a machine vision system used in the quality control of acrylic fibre production. The system was developed by INETI under a contract with a leading industrial manufacturer of acrylic fibres. It monitors several parameters of the acrylic production process. This paper presents, after a brief overview of the system, a detailed description of the machine vision algorithms developed to perform the inspection tasks unique to this system. Some of the results of online operation are also presented.

Modeling of Electrochemical Copying in a Finite-Width Cell

NASA Astrophysics Data System (ADS)

Zhitnikov, V. P.; Sherykhalina, N. M.; Zaripov, A. A.

2017-11-01

The problem of modeling of electrochemical machining is reduced to the solution of the Schwartz problem on a parametrical rectangle with the use of theta-functions. Various conditions (non-equipotentiality of electrodes and inconstancy of current efficiency) at the boundary of a processed surface are considered. Nonstationary, quasistationary, stationary, and limit solutions are studied. Results of machining of surfaces by tool electrodes of various shapes are given. It is shown that machining mode parameters significantly affect the dissolved layer size necessary for obtaining high-precision copying.

Machine learning phases of matter

NASA Astrophysics Data System (ADS)

Carrasquilla, Juan; Stoudenmire, Miles; Melko, Roger

We show how the technology that allows automatic teller machines read hand-written digits in cheques can be used to encode and recognize phases of matter and phase transitions in many-body systems. In particular, we analyze the (quasi-)order-disorder transitions in the classical Ising and XY models. Furthermore, we successfully use machine learning to study classical Z2 gauge theories that have important technological application in the coming wave of quantum information technologies and whose phase transitions have no conventional order parameter.

The design and improvement of radial tire molding machine

NASA Astrophysics Data System (ADS)

Wang, Wenhao; Zhang, Tao

2018-04-01

This paper presented that the high accuracy semisteel meridian tire molding machine structure configurations, combining tyre high precision characteristics, the original structure and parameter optimization, technology improvement innovation design period of opening and closing machine rotary shaping drum institutions. This way out of the shaft from the structure to the push-pull type movable shaping drum of thinking limit, compared with the specifications and shaping drum can smaller contraction, is conducive to forming the tire and reduce the tire deformation.

Minimization of the hole overcut and cylindricity errors during rotary ultrasonic drilling of Ti-6Al-4V

NASA Astrophysics Data System (ADS)

Nasr, M.; Anwar, S.; El-Tamimi, A.; Pervaiz, S.

2018-04-01

Titanium and its alloys e.g. Ti6Al4V have widespread applications in aerospace, automotive and medical industry. At the same time titanium and its alloys are regarded as difficult to machine materials due to their high strength and low thermal conductivity. Significant efforts have been dispensed to improve the accuracy of the machining processes for Ti6Al4V. The current study present the use of the rotary ultrasonic drilling (RUD) process for machining high quality holes in Ti6Al4V. The study takes into account the effects of the main RUD input parameters including spindle speed, ultrasonic power, feed rate and tool diameter on the key output responses related to the accuracy of the drilled holes including cylindricity and overcut errors. Analysis of variance (ANOVA) was employed to study the influence of the input parameters on cylindricity and overcut error. Later, regression models were developed to find the optimal set of input parameters to minimize the cylindricity and overcut errors.

Machining of AISI D2 Tool Steel with Multiple Hole Electrodes by EDM Process

NASA Astrophysics Data System (ADS)

Prasad Prathipati, R.; Devuri, Venkateswarlu; Cheepu, Muralimohan; Gudimetla, Kondaiah; Uzwal Kiran, R.

2018-03-01

In recent years, with the increasing of technology the demand for machining processes is increasing for the newly developed materials. The conventional machining processes are not adequate to meet the accuracy of the machining of these materials. The non-conventional machining processes of electrical discharge machining is one of the most efficient machining processes is being widely used to machining of high accuracy products of various industries. The optimum selection of process parameters is very important in machining processes as that of an electrical discharge machining as they determine surface quality and dimensional precision of the obtained parts, even though time consumption rate is higher for machining of large dimension features. In this work, D2 high carbon and chromium tool steel has been machined using electrical discharge machining with the multiple hole electrode technique. The D2 steel has several applications such as forming dies, extrusion dies and thread rolling. But the machining of this tool steel is very hard because of it shard alloyed elements of V, Cr and Mo which enhance its strength and wear properties. However, the machining is possible by using electrical discharge machining process and the present study implemented a new technique to reduce the machining time using a multiple hole copper electrode. In this technique, while machining with multiple holes electrode, fin like projections are obtained, which can be removed easily by chipping. Then the finishing is done by using solid electrode. The machining time is reduced to around 50% while using multiple hole electrode technique for electrical discharge machining.

30 CFR 48.27 - Training of miners assigned to a task in which they have had no previous experience; minimum...

Code of Federal Regulations, 2012 CFR

2012-07-01

..., drilling machine operators, haulage and conveyor systems operators, ground control machine operators, AMS... practice in the assigned tasks, and the performance of work duties at times or places where production is..., while under direct and immediate supervision and production is in progress, operation of the machine or...

30 CFR 48.27 - Training of miners assigned to a task in which they have had no previous experience; minimum...

Code of Federal Regulations, 2011 CFR

2011-07-01

..., drilling machine operators, haulage and conveyor systems operators, ground control machine operators, AMS... practice in the assigned tasks, and the performance of work duties at times or places where production is..., while under direct and immediate supervision and production is in progress, operation of the machine or...

30 CFR 48.27 - Training of miners assigned to a task in which they have had no previous experience; minimum...

Code of Federal Regulations, 2014 CFR

2014-07-01

..., drilling machine operators, haulage and conveyor systems operators, ground control machine operators, AMS... practice in the assigned tasks, and the performance of work duties at times or places where production is..., while under direct and immediate supervision and production is in progress, operation of the machine or...

30 CFR 48.27 - Training of miners assigned to a task in which they have had no previous experience; minimum...

Code of Federal Regulations, 2013 CFR

2013-07-01

..., drilling machine operators, haulage and conveyor systems operators, ground control machine operators, AMS... practice in the assigned tasks, and the performance of work duties at times or places where production is..., while under direct and immediate supervision and production is in progress, operation of the machine or...

Technology of machine tools. Volume 1. Executive summary

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

The Physics and Physical Chemistry of Molecular Machines.

PubMed

Astumian, R Dean; Mukherjee, Shayantani; Warshel, Arieh

2016-06-17

The concept of a "power stroke"-a free-energy releasing conformational change-appears in almost every textbook that deals with the molecular details of muscle, the flagellar rotor, and many other biomolecular machines. Here, it is shown by using the constraints of microscopic reversibility that the power stroke model is incorrect as an explanation of how chemical energy is used by a molecular machine to do mechanical work. Instead, chemically driven molecular machines operating under thermodynamic constraints imposed by the reactant and product concentrations in the bulk function as information ratchets in which the directionality and stopping torque or stopping force are controlled entirely by the gating of the chemical reaction that provides the fuel for the machine. The gating of the chemical free energy occurs through chemical state dependent conformational changes of the molecular machine that, in turn, are capable of generating directional mechanical motions. In strong contrast to this general conclusion for molecular machines driven by catalysis of a chemical reaction, a power stroke may be (and often is) an essential component for a molecular machine driven by external modulation of pH or redox potential or by light. This difference between optical and chemical driving properties arises from the fundamental symmetry difference between the physics of optical processes, governed by the Bose-Einstein relations, and the constraints of microscopic reversibility for thermally activated processes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Design of bearings for rotor systems based on stability

NASA Technical Reports Server (NTRS)

Dhar, D.; Barrett, L. E.; Knospe, C. R.

1992-01-01

Design of rotor systems incorporating stable behavior is of great importance to manufacturers of high speed centrifugal machinery since destabilizing mechanisms (from bearings, seals, aerodynamic cross coupling, noncolocation effects from magnetic bearings, etc.) increase with machine efficiency and power density. A new method of designing bearing parameters (stiffness and damping coefficients or coefficients of the controller transfer function) is proposed, based on a numerical search in the parameter space. The feedback control law is based on a decentralized low order controller structure, and the various design requirements are specified as constraints in the specification and parameter spaces. An algorithm is proposed for solving the problem as a sequence of constrained 'minimax' problems, with more and more eigenvalues into an acceptable region in the complex plane. The algorithm uses the method of feasible directions to solve the nonlinear constrained minimization problem at each stage. This methodology emphasizes the designer's interaction with the algorithm to generate acceptable designs by relaxing various constraints and changing initial guesses interactively. A design oriented user interface is proposed to facilitate the interaction.

Parameter identification and optimization of slide guide joint of CNC machine tools

NASA Astrophysics Data System (ADS)

Zhou, S.; Sun, B. B.

2017-11-01

The joint surface has an important influence on the performance of CNC machine tools. In order to identify the dynamic parameters of slide guide joint, the parametric finite element model of the joint is established and optimum design method is used based on the finite element simulation and modal test. Then the mode that has the most influence on the dynamics of slip joint is found through harmonic response analysis. Take the frequency of this mode as objective, the sensitivity analysis of the stiffness of each joint surface is carried out using Latin Hypercube Sampling and Monte Carlo Simulation. The result shows that the vertical stiffness of slip joint surface constituted by the bed and the slide plate has the most obvious influence on the structure. Therefore, this stiffness is taken as the optimization variable and the optimal value is obtained through studying the relationship between structural dynamic performance and stiffness. Take the stiffness values before and after optimization into the FEM of machine tool, and it is found that the dynamic performance of the machine tool is improved.

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

Two-component end mills with multilayer composite nano-structured coatings as a viable alternative to monolithic carbide end mills

NASA Astrophysics Data System (ADS)

Vereschaka, Alexey; Mokritskii, Boris; Mokritskaya, Elena; Sharipov, Oleg; Oganyan, Maksim

2018-03-01

The paper deals with the challenges of the application of two-component end mills, which represent a combination of a carbide cutting part and a shank made of cheaper structural material. The calculations of strains and deformations of composite mills were carried out in comparison with solid carbide mills, with the use of the finite element method. The study also involved the comparative analysis of accuracy parameters of machining with monolithic mills and two-component mills with various shank materials. As a result of the conducted cutting tests in milling aluminum alloy with monolithic and two-component end mills with specially developed multilayer composite nano-structured coatings, it has been found that the use of such coatings can reduce strains and, correspondingly, deformations, which can improve the accuracy of machining. Thus, the application of two-component end mills with multilayer composite nano-structured coatings can provide a reduction in the cost of machining while maintaining or even improving the tool life and machining accuracy parameters.

Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories

NASA Astrophysics Data System (ADS)

Matsunaga, Y.; Sugita, Y.

2018-06-01

A data-driven modeling scheme is proposed for conformational dynamics of biomolecules based on molecular dynamics (MD) simulations and experimental measurements. In this scheme, an initial Markov State Model (MSM) is constructed from MD simulation trajectories, and then, the MSM parameters are refined using experimental measurements through machine learning techniques. The second step can reduce the bias of MD simulation results due to inaccurate force-field parameters. Either time-series trajectories or ensemble-averaged data are available as a training data set in the scheme. Using a coarse-grained model of a dye-labeled polyproline-20, we compare the performance of machine learning estimations from the two types of training data sets. Machine learning from time-series data could provide the equilibrium populations of conformational states as well as their transition probabilities. It estimates hidden conformational states in more robust ways compared to that from ensemble-averaged data although there are limitations in estimating the transition probabilities between minor states. We discuss how to use the machine learning scheme for various experimental measurements including single-molecule time-series trajectories.

Development of Processing Parameters for Organic Binders Using Selective Laser Sintering

NASA Technical Reports Server (NTRS)

Mobasher, Amir A.

2003-01-01

This document describes rapid prototyping, its relation to Computer Aided Design (CAD), and the application of these techniques to choosing parameters for Selective Laser Sintering (SLS). The document reviews the parameters selected by its author for his project, the SLS machine used, and its software.

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

NASA Astrophysics Data System (ADS)

Pasquato, Mario; Chung, Chul

2016-05-01

Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims: We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods: We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results: The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Speech emotion recognition methods: A literature review

NASA Astrophysics Data System (ADS)

Basharirad, Babak; Moradhaseli, Mohammadreza

2017-10-01

Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

Statistical machine translation for biomedical text: are we there yet?

PubMed

Wu, Cuijun; Xia, Fei; Deleger, Louise; Solti, Imre

2011-01-01

In our paper we addressed the research question: "Has machine translation achieved sufficiently high quality to translate PubMed titles for patients?". We analyzed statistical machine translation output for six foreign language - English translation pairs (bi-directionally). We built a high performing in-house system and evaluated its output for each translation pair on large scale both with automated BLEU scores and human judgment. In addition to the in-house system, we also evaluated Google Translate's performance specifically within the biomedical domain. We report high performance for German, French and Spanish -- English bi-directional translation pairs for both Google Translate and our system.

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

PubMed Central

Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

2016-01-01

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs). PMID:26985826

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

PubMed

Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

2016-01-01

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

Electromechanical modeling and experimental verification of a directly printed nanocomposite

NASA Astrophysics Data System (ADS)

Nafari, Alireza; Sodano, Henry A.

2018-03-01

Piezoelectric materials are currently among the most promising building blocks of sensing, actuating and energy harvesting systems. However, these materials are limited in applications due to difficulty in machining and casting it on to curve surfaces. To mitigate this issue, one method is through additive manufacturing (direct printing) of piezoelectric nanocomposite in which piezoelectric nanomaterials are embedded into a polymer matrix. Although significant progress has been recently made in this area, modeling the electromechanical response of a directly printed nanocomposite remains a challenge. Thus the objective of this study is to develop robust micromechanical and finite element models that allows the study of the electroelastic properties of a directly printed nanocomposite containing piezoelectric inclusions. Furthermore, the dependence of these properties on geometrical parameters such as aspect ratio and alignment of the active phase are investigated. The focus of this work is a demonstration of the effect gradual alignment of piezoelectric nanowires in a nanocomposite from randomly oriented to purely aligned improves the electroelastic properties of a directly printed nanocomposite. Finally, these models are verified through experimental measurement of electroelastic properties of the nanocomposites containing barium titanate nanowires in Polydimethylsiloxane (PDMS) polymer.

A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

NASA Astrophysics Data System (ADS)

Ali, Salah M.; Hui, K. H.; Hee, L. M.; Salman Leong, M.; Al-Obaidi, M. A.; Ali, Y. H.; Abdelrhman, Ahmed M.

2018-03-01

Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.

Surface quality and topographic inspection of variable compliance part after precise turning

NASA Astrophysics Data System (ADS)

Nieslony, P.; Krolczyk, G. M.; Wojciechowski, S.; Chudy, R.; Zak, K.; Maruda, R. W.

2018-03-01

The paper presents the problem of precise turning of the mould parts with variable compliance and demonstrates a topographic inspection of the machined surface quality. The study was conducted for the cutting tools made of cemented carbide with coatings, in a range of variable cutting parameters. The long shaft with special axial hole, made of hardened 55NiCrMoV6 steel was selected as a workpiece. The carried out study included the stiffness measurement of the machining system, as well as the investigation of cutting force components. In this context, the surface topography parameters were evaluated using the stylus profile meter and analysed. The research revealed that the surface topography, alongside the 3D functional parameters, and PSD influences the performance of the machined surface. The lowest surface roughness parameters values, equalled to Sa = 1 μm and Sz = 4.3 μm have been obtained during turning with cutting speed vc = 90 m/min. The stable turning of variable compliance part affects the surface texture formation with a unidirectional perpendicular, anisotropic structure. Nevertheless, in case of unstable turning, the characteristic chatter marks are observed, and process dynamics has greater contribution in formation of surface finish than turning kinematics and elastic plastic deformation of workpiece.

Analysis of machining accuracy during free form surface milling simulation for different milling strategies

NASA Astrophysics Data System (ADS)

Matras, A.; Kowalczyk, R.

2014-11-01

The analysis results of machining accuracy after the free form surface milling simulations (based on machining EN AW- 7075 alloys) for different machining strategies (Level Z, Radial, Square, Circular) are presented in the work. Particular milling simulations were performed using CAD/CAM Esprit software. The accuracy of obtained allowance is defined as a difference between the theoretical surface of work piece element (the surface designed in CAD software) and the machined surface after a milling simulation. The difference between two surfaces describes a value of roughness, which is as the result of tool shape mapping on the machined surface. Accuracy of the left allowance notifies in direct way a surface quality after the finish machining. Described methodology of usage CAD/CAM software can to let improve a time design of machining process for a free form surface milling by a 5-axis CNC milling machine with omitting to perform the item on a milling machine in order to measure the machining accuracy for the selected strategies and cutting data.

Results of Investigative Tests of Gas Turbine Engine Compressor Blades Obtained by Electrochemical Machining

NASA Astrophysics Data System (ADS)

Kozhina, T. D.; Kurochkin, A. V.

2016-04-01

The paper highlights results of the investigative tests of GTE compressor Ti-alloy blades obtained by the method of electrochemical machining with oscillating tool-electrodes, carried out in order to define the optimal parameters of the ECM process providing attainment of specified blade quality parameters given in the design documentation, while providing maximal performance. The new technological methods suggested based on the results of the tests; in particular application of vibrating tool-electrodes and employment of locating elements made of high-strength materials, significantly extend the capabilities of this method.

Stability analysis of free piston Stirling engines

NASA Astrophysics Data System (ADS)

Bégot, Sylvie; Layes, Guillaume; Lanzetta, François; Nika, Philippe

2013-03-01

This paper presents a stability analysis of a free piston Stirling engine. The model and the detailed calculation of pressures losses are exposed. Stability of the machine is studied by the observation of the eigenvalues of the model matrix. Model validation based on the comparison with NASA experimental results is described. The influence of operational and construction parameters on performance and stability issues is exposed. The results show that most parameters that are beneficial for machine power seem to induce irregular mechanical characteristics with load, suggesting that self-sustained oscillations could be difficult to maintain and control.

Research on the processing technology of elongated holes based on rotary ultrasonic drilling

NASA Astrophysics Data System (ADS)

Tong, Yi; Chen, Jianhua; Sun, Lipeng; Yu, Xin; Wang, Xin

2014-08-01

The optical glass is hard, brittle and difficult to process. Based on the method of rotating ultrasonic drilling, the study of single factor on drilling elongated holes was made in optical glass. The processing equipment was DAMA ultrasonic machine, and the machining tools were electroplated with diamond. Through the detection and analysis on the processing quality and surface roughness, the process parameters (the spindle speed, amplitude, feed rate) of rotary ultrasonic drilling were researched, and the influence of processing parameters on surface roughness was obtained, which will provide reference and basis for the actual processing.

Design synthesis and optimization of permanent magnet synchronous machines based on computationally-efficient finite element analysis

NASA Astrophysics Data System (ADS)

Sizov, Gennadi Y.

In this dissertation, a model-based multi-objective optimal design of permanent magnet ac machines, supplied by sine-wave current regulated drives, is developed and implemented. The design procedure uses an efficient electromagnetic finite element-based solver to accurately model nonlinear material properties and complex geometric shapes associated with magnetic circuit design. Application of an electromagnetic finite element-based solver allows for accurate computation of intricate performance parameters and characteristics. The first contribution of this dissertation is the development of a rapid computational method that allows accurate and efficient exploration of large multi-dimensional design spaces in search of optimum design(s). The computationally efficient finite element-based approach developed in this work provides a framework of tools that allow rapid analysis of synchronous electric machines operating under steady-state conditions. In the developed modeling approach, major steady-state performance parameters such as, winding flux linkages and voltages, average, cogging and ripple torques, stator core flux densities, core losses, efficiencies and saturated machine winding inductances, are calculated with minimum computational effort. In addition, the method includes means for rapid estimation of distributed stator forces and three-dimensional effects of stator and/or rotor skew on the performance of the machine. The second contribution of this dissertation is the development of the design synthesis and optimization method based on a differential evolution algorithm. The approach relies on the developed finite element-based modeling method for electromagnetic analysis and is able to tackle large-scale multi-objective design problems using modest computational resources. Overall, computational time savings of up to two orders of magnitude are achievable, when compared to current and prevalent state-of-the-art methods. These computational savings allow one to expand the optimization problem to achieve more complex and comprehensive design objectives. The method is used in the design process of several interior permanent magnet industrial motors. The presented case studies demonstrate that the developed finite element-based approach practically eliminates the need for using less accurate analytical and lumped parameter equivalent circuit models for electric machine design optimization. The design process and experimental validation of the case-study machines are detailed in the dissertation.

Effect of the determination method of the material parameters on the accuracy of the hole expansion simulation for cold rolled steel sheet

NASA Astrophysics Data System (ADS)

Nakano, Hayato; Hakoyama, Tomoyuki; Kuwabara, Toshihiko

2017-10-01

Hole expansion forming of a cold rolled steel sheet is investigated both experimentally and analytically to clarify the effects of material models on the predictive accuracy of finite element analyses (FEA). The multiaxial plastic deformation behavior of a cold rolled steel sheet with a thickness of 1.2 mm was measured using a servo-controlled multiaxial tube expansion testing machine for the range of strain from initial yield to fracture. Tubular specimens were fabricated from the sheet sample by roller bending and laser welding. Many linear stress paths in the first quadrant of stress space were applied to the tubular specimens to measure the contours of plastic work in stress space up to a reference plastic strain of 0.24 along with the directions of plastic strain rates. The anisotropic parameters and exponent of the Yld2000-2d yield function (Barlat et al., 2003) were optimized to approximate the contours of plastic work and the directions of plastic strain rates. The hole expansion forming simulations were performed using the different model identifications based on the Yld2000-2d yield function. It is concluded that the yield function best capturing both the plastic work contours and the directions of plastic strain rates leads to the most accurate predicted FEA.

Technical Note: Using experimentally determined proton spot scanning timing parameters to accurately model beam delivery time.

PubMed

Shen, Jiajian; Tryggestad, Erik; Younkin, James E; Keole, Sameer R; Furutani, Keith M; Kang, Yixiu; Herman, Michael G; Bues, Martin

2017-10-01

To accurately model the beam delivery time (BDT) for a synchrotron-based proton spot scanning system using experimentally determined beam parameters. A model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x- and y-directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (T BDT ) were compared with the BDTs recorded in the treatment delivery log files (T Log ): ∆t = T Log -T BDT . The experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x-direction and 19.3 m/s in y-direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = -0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = -7.48 ± 6.97 s). An accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may provide guidance on how to effectively reduce BDT and may be used to identifying deteriorating machine performance. © 2017 American Association of Physicists in Medicine.

Damage detection in rotating machinery by means of entropy-based parameters

NASA Astrophysics Data System (ADS)

Tocarciuc, Alexandru; Bereteu, Liviu; ǎgǎnescu, Gheorghe Eugen, Dr

2014-11-01

The paper is proposing two new entropy-based parameters, namely Renyi Entropy Index (REI) and Sharma-Mittal Entropy Index (SMEI), for detecting the presence of failures (or damages) in rotating machinery, namely: belt structural damage, belt wheels misalignment, failure of the fixing bolt of the machine to its baseplate and eccentricities (i.e.: due to detaching a small piece of material or bad mounting of the rotating components of the machine). The algorithms to obtain the proposed entropy-based parameters are described and test data is used in order to assess their sensitivity. A vibration test bench is used for measuring the levels of vibration while artificially inducing damage. The deviation of the two entropy-based parameters is compared in two states of the vibration test bench: not damaged and damaged. At the end of the study, their sensitivity is compared to Shannon Entropic Index.

High-efficiency machining methods for aviation materials

NASA Astrophysics Data System (ADS)

Kononov, V. K.

1991-07-01

The papers contained in this volume present results of theoretical and experimental studies aimed at increasing the efficiency of cutting tools during the machining of high-temperature materials and titanium alloys. Specific topics discussed include a study of the performance of disk cutters during the machining of flexible parts of a high-temperature alloy, VZhL14N; a study of the wear resistance of cutters of hard alloys of various types; effect of a deformed electric field on the precision of the electrochemical machining of gas turbine engine components; and efficient machining of parts of composite materials. The discussion also covers the effect of the technological process structure on the residual stress distribution in the blades of gas turbine engines; modeling of the multiparameter assembly of engineering products for a specified priority of geometrical output parameters; and a study of the quality of the surface and surface layer of specimens machined by a high-temperature pulsed plasma.

Application de la methode de la reponse frequentielle a l'arret "SSFR", sur une machine synchrone a poles saillants de grande puissance

NASA Astrophysics Data System (ADS)

Belqorchi, Abdelghafour

Forty years after Watson and Manchur conducted the Stand-Still Frequency Response (SSFR) test on a large turbogenerator, the applicability of this technic on a powerful salient pole synchronous generator has yet to be confirmed. The scientific literature on the subject is rare and very few have attempted to compare SSFR parameter results with those deduced by classical tests. The validity of SSFR on large salient pole machines has still to be proven. The present work aims in participating to fill this knowledge gap. It can be used to build a database of measurements highly needed to draw the validity of the technic. Also, the author hopes to demonstrate the potential of SSFR model to represent the machine, not only in cases of weak disturbances but also strong ones such as instantaneous three-phase short-circuit faults. The difficulties raised by previous searchers are: The lack of accuracy in very low frequency measurements; The difficulty in rotor positioning, according to d and q axes, in case of salient pole machines; The measurement current level influence on magnetizing inductances, in axes-d and; The rotation impact on damper circuits for some rotors design. Aware of the above difficulties, the author conducted an SSFR test on a large salient pole machine (285 MVA). The generator under test has laminated non isolated rotor and an integral slot number. The damper windings in adjacent poles are connected together, via the polar core and the rotor rim. Finally, the damping circuit is unaffected by rotation. To improve the measurement accuracy, in very low frequencies, the most precise frequency response analyser available on the market was used. Besides, the frequency responses of the signals conditioning modules (i.e., isolation, amplification...) were accounted for to correct the four measured SSFR transfer functions. Immunization against noise and use of instrumentation in their optimum range, were other technics rigorously applied. Magnetizing inductances, being influenced by the measurement current magnitude, the latter was maintained constant in the range 1mHz-20Hz. Other problems such as the rotation impact on damper circuits or the difficulty of rotor positioning are eliminated or attenuated by the intrinsic characteristics of the machine. Regarding the data analysis, the Maximum Likelihood Estimation (MLE) method was used to determine the third and second order equivalent circuit from SSFR measurements. In d-axis, the approaches of adjustment to two and three transfer functions (Ld(s), sG(s) and Lafo(s)) were explored. The second order model, derived from (Ld( s) and G(s)), was used to deduce the machine standard parameters. The latter were compared with the values given by the manufacturer and by conventional on-site tests: Instantaneous three-phase short-circuit, Dalton-Cameron and the d-axis transient time constant at open stator (T'do). The comparison showed the good accuracy of SSFR values. Subsequently, a machine model was built in EMTP-RV based on SSFR standard parameters. The model was able to reproduce stator and rotor currents measured during instantaneous three-phase short-circuit test. Some adjustments, to SSFR parameters, were needed to reproduce stator voltage and rotor current acquired during load rejection d-axis test. It is worthwhile noting that the load rejection d-axis test, recently added to IEEE 115-2009 annex, must be modified to take into account the saturation and excitation impedance impact on deduced parameters. Regarding this issue, some suggestions are proposed by the author. The obtained SSFR results, contribute to raise confidence on SSFR application on large salient pole machines. In addition, it shows the aptitude of the SSFR model to represent the machine in both cases of weak and strong disturbances, at least on machines similar the one studied. Index Terms: Salient pole, frequency response, SSFR, equivalent circuit, operational inductance.

Machine Learning

DTIC Science & Technology

1990-04-01

DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

Development of techniques to enhance man/machine communication

NASA Technical Reports Server (NTRS)

Targ, R.; Cole, P.; Puthoff, H.

1974-01-01

A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

A comparison of methods using optical coherence tomography to detect demineralized regions in teeth

PubMed Central

Sowa, Michael G.; Popescu, Dan P.; Friesen, Jeri R.; Hewko, Mark D.; Choo-Smith, Lin-P’ing

2013-01-01

Optical coherence tomography (OCT) is a three- dimensional optical imaging technique that can be used to identify areas of early caries formation in dental enamel. The OCT signal at 850 nm back-reflected from sound enamel is attenuated stronger than the signal back-reflected from demineralized regions. To quantify this observation, the OCT signal as a function of depth into the enamel (also known as the A-scan intensity), the histogram of the A-scan intensities and three summary parameters derived from the A-scan are defined and their diagnostic potential compared. A total of 754 OCT A-scans were analyzed. The three summary parameters derived from the A-scans, the OCT attenuation coefficient as well as the mean and standard deviation of the lognormal fit to the histogram of the A-scan ensemble show statistically significant differences (p < 0.01) when comparing parameters from sound enamel and caries. Furthermore, these parameters only show a modest correlation. Based on the area under the curve (AUC) of the receiver operating characteristics (ROC) plot, the OCT attenuation coefficient shows higher discriminatory capacity (AUC=0.98) compared to the parameters derived from the lognormal fit to the histogram of the A-scan. However, direct analysis of the A-scans or the histogram of A-scan intensities using linear support vector machine classification shows diagnostic discrimination (AUC = 0.96) comparable to that achieved using the attenuation coefficient. These findings suggest that either direct analysis of the A-scan, its intensity histogram or the attenuation coefficient derived from the descending slope of the OCT A-scan have high capacity to discriminate between regions of caries and sound enamel. PMID:22052833

Characterization and Effects of Fiber Pull-Outs in Hole Quality of Carbon Fiber Reinforced Plastics Composite

PubMed Central

Alizadeh Ashrafi, Sina; Miller, Peter W.; Wandro, Kevin M.; Kim, Dave

2016-01-01

Hole quality plays a crucial role in the production of close-tolerance holes utilized in aircraft assembly. Through drilling experiments of carbon fiber-reinforced plastic composites (CFRP), this study investigates the impact of varying drilling feed and speed conditions on fiber pull-out geometries and resulting hole quality parameters. For this study, hole quality parameters include hole size variance, hole roundness, and surface roughness. Fiber pull-out geometries are quantified by using scanning electron microscope (SEM) images of the mechanically-sectioned CFRP-machined holes, to measure pull-out length and depth. Fiber pull-out geometries and the hole quality parameter results are dependent on the drilling feed and spindle speed condition, which determines the forces and undeformed chip thickness during the process. Fiber pull-out geometries influence surface roughness parameters from a surface profilometer, while their effect on other hole quality parameters obtained from a coordinate measuring machine is minimal. PMID:28773950

Machine learning for science: state of the art and future prospects.

PubMed

Mjolsness, E; DeCoste, D

2001-09-14

Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions.

Machine learning approaches in medical image analysis: From detection to diagnosis.

PubMed

de Bruijne, Marleen

2016-10-01

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mou, J.I.; King, C.

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess themore » status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.« less

Rapid Prototyping in Technology Education.

ERIC Educational Resources Information Center

Flowers, Jim; Moniz, Matt

2002-01-01

Describes how technology education majors are using a high-tech model builder, called a fused deposition modeling machine, to develop their models directly from computer-based designs without any machining. Gives examples of applications in technology education. (JOW)

Energy landscapes for a machine-learning prediction of patient discharge

NASA Astrophysics Data System (ADS)

Das, Ritankar; Wales, David J.

2016-06-01

The energy landscapes framework is applied to a configuration space generated by training the parameters of a neural network. In this study the input data consists of time series for a collection of vital signs monitored for hospital patients, and the outcomes are patient discharge or continued hospitalisation. Using machine learning as a predictive diagnostic tool to identify patterns in large quantities of electronic health record data in real time is a very attractive approach for supporting clinical decisions, which have the potential to improve patient outcomes and reduce waiting times for discharge. Here we report some preliminary analysis to show how machine learning might be applied. In particular, we visualize the fitting landscape in terms of locally optimal neural networks and the connections between them in parameter space. We anticipate that these results, and analogues of thermodynamic properties for molecular systems, may help in the future design of improved predictive tools.

Computational Fluid Dynamics Analysis of Nozzle in Abrasive Water Jet Machining

NASA Astrophysics Data System (ADS)

Venugopal, S.; Chandresekaran, M.; Muthuraman, V.; Sathish, S.

2017-03-01

Abrasive water jet cutting is one of the most recently developed non-traditional manufacturing technologies. The general nature of flow through the machining, results in rapid wear of the nozzle which decrease the cutting performance. It is well known that the inlet pressure of the abrasive water suspension has main effect on the erosion characteristics of the inner surface of the nozzle. The objective of the project is to analyze the effect of inlet pressure on wall shear and exit kinetic energy. The analysis would be carried out by varying the inlet pressure of the nozzle, so as to obtain optimized process parameters for minimum nozzle wear. The two phase flow analysis would be carried by using computational fluid dynamics tool CFX. The availability of minimized process parameters such as of abrasive water jet machining (AWJM) is limited to water and experimental test can be cost prohibitive.

Prediction of multi performance characteristics of wire EDM process using grey ANFIS

NASA Astrophysics Data System (ADS)

Kumanan, Somasundaram; Nair, Anish

2017-09-01

Super alloys are used to fabricate components in ultra-supercritical power plants. These hard to machine materials are processed using non-traditional machining methods like Wire cut electrical discharge machining and needs attention. This paper details about multi performance optimization of wire EDM process using Grey ANFIS. Experiments are designed to establish the performance characteristics of wire EDM such as surface roughness, material removal rate, wire wear rate and geometric tolerances. The control parameters are pulse on time, pulse off time, current, voltage, flushing pressure, wire tension, table feed and wire speed. Grey relational analysis is employed to optimise the multi objectives. Analysis of variance of the grey grades is used to identify the critical parameters. A regression model is developed and used to generate datasets for the training of proposed adaptive neuro fuzzy inference system. The developed prediction model is tested for its prediction ability.

Development of Semi-Automatic Lathe by using Intelligent Soft Computing Technique

NASA Astrophysics Data System (ADS)

Sakthi, S.; Niresh, J.; Vignesh, K.; Anand Raj, G.

2018-03-01

This paper discusses the enhancement of conventional lathe machine to semi-automated lathe machine by implementing a soft computing method. In the present scenario, lathe machine plays a vital role in the engineering division of manufacturing industry. While the manual lathe machines are economical, the accuracy and efficiency are not up to the mark. On the other hand, CNC machine provide the desired accuracy and efficiency, but requires a huge capital. In order to over come this situation, a semi-automated approach towards the conventional lathe machine is developed by employing stepper motors to the horizontal and vertical drive, that can be controlled by Arduino UNO -microcontroller. Based on the input parameters of the lathe operation the arduino coding is been generated and transferred to the UNO board. Thus upgrading from manual to semi-automatic lathe machines can significantly increase the accuracy and efficiency while, at the same time, keeping a check on investment cost and consequently provide a much needed escalation to the manufacturing industry.

Fully automated measuring setup for tactile coordinate measuring machine for three dimensional measurement of freeform eyeglass frames

NASA Astrophysics Data System (ADS)

Rückwardt, M.; Göpfert, A.; Correns, M.; Schellhorn, M.; Linß, G.

2010-07-01

Coordinate measuring machines are high precession all-rounder in three dimensional measuring. Therefore the versatility of parameters and expandability of additionally hardware is very comprehensive. Consequently you need much expert knowledge of the user and mostly a lot of advanced information about the measuring object. In this paper a coordinate measuring machine and a specialized measuring machine are compared at the example of the measuring of eyeglass frames. For this case of three dimensional measuring challenges the main focus is divided into metrological and economical aspects. At first there is shown a fully automated method for tactile measuring of this abstract form. At second there is shown a comparison of the metrological characteristics of a coordinate measuring machine and a tracer for eyeglass frames. The result is in favour to the coordinate measuring machine. It was not surprising in these aspects. At last there is shown a comparison of the machine in front of the economical aspects.

Learning surface molecular structures via machine vision

NASA Astrophysics Data System (ADS)

Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.

2017-08-01

Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (`read out') all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds and thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.

Overview Of Dry-Etch Techniques

NASA Astrophysics Data System (ADS)

Salzer, John M.

1986-08-01

With pattern dimensions shrinking, dry methods of etching providing controllable degrees of anisotropy become a necessity. A number of different configurations of equipment - inline, hex, planar, barrel - have been offered, and within each type, there are numerous significant variations. Further, each specific type of machine must be perfected over a complex, interactive parameter space to achieve suitable removal of various materials. Among the most critical system parameters are the choice of cathode or anode to hold the wafers, the chamber pressure, the plasma excitation frequency, and the electrode and magnetron structures. Recent trends include the use of vacuum load locks, multiple chambers, multiple electrodes, downstream etching or stripping, and multistep processes. A major percentage of etches in production handle the three materials: polysilicon, oxide and aluminum. Recent process developments have targeted refractory metals, their silicides, and with increasing emphasis, silicon trenching. Indeed, with new VLSI structures, silicon trenching has become the process of greatest interest. For stripping, dry processes provide advantages other than anisotropy. Here, too, new configurations and methods have been introduced recently. While wet processes are less than desirable from a number of viewpoints (handling, safety, disposal, venting, classes of clean room, automatability), dry methods are still being perfected as a direct, universal replacement. The paper will give an overview of these machine structures and process solutions, together with examples of interest. These findings and the trends discussed are based on semiannual survey of manufacturers and users of the various types of equipment.

A Pole-Zero Filter Cascade Provides Good Fits to Human Masking Data and to Basilar Membrane and Neural Data

NASA Astrophysics Data System (ADS)

Lyon, Richard F.

2011-11-01

A cascade of two-pole-two-zero filters with level-dependent pole and zero dampings, with few parameters, can provide a good match to human psychophysical and physiological data. The model has been fitted to data on detection threshold for tones in notched-noise masking, including bandwidth and filter shape changes over a wide range of levels, and has been shown to provide better fits with fewer parameters compared to other auditory filter models such as gammachirps. Originally motivated as an efficient machine implementation of auditory filtering related to the WKB analysis method of cochlear wave propagation, such filter cascades also provide good fits to mechanical basilar membrane data, and to auditory nerve data, including linear low-frequency tail response, level-dependent peak gain, sharp tuning curves, nonlinear compression curves, level-independent zero-crossing times in the impulse response, realistic instantaneous frequency glides, and appropriate level-dependent group delay even with minimum-phase response. As part of exploring different level-dependent parameterizations of such filter cascades, we have identified a simple sufficient condition for stable zero-crossing times, based on the shifting property of the Laplace transform: simply move all the s-domain poles and zeros by equal amounts in the real-s direction. Such pole-zero filter cascades are efficient front ends for machine hearing applications, such as music information retrieval, content identification, speech recognition, and sound indexing.

The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learning

NASA Astrophysics Data System (ADS)

Caron, Sascha; Kim, Jong Soo; Rolbiecki, Krzysztof; de Austri, Roberto Ruiz; Stienen, Bob

2017-04-01

A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300, 000 pMSSM model sets - each tested against 200 signal regions by ATLAS - have been used to train and validate SUSY-AI. The code is currently able to reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at least 93%. It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/.

Direct Torque Control of a Three-Phase Voltage Source Inverter-Fed Induction Machine

DTIC Science & Technology

2013-12-01

factors, FOC acquires all advantages of DC machine control and frees itself from the mechanical commutation drawbacks. Furthermore, FOC leads to high...of three-phase induction motor using microcontroller,” S.R.M Engineering College, Tamil Nadu, India , June/July 2006. [5] Texas Instruments Europe...loop. Direct flux control is possible through the constant magnetic field orientation achieved through commutator action. These two primary factors

Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters.

PubMed

Zhou, Zhiguo; Folkert, Michael; Cannon, Nathan; Iyengar, Puneeth; Westover, Kenneth; Zhang, Yuanyuan; Choy, Hak; Timmerman, Robert; Yan, Jingsheng; Xie, Xian-J; Jiang, Steve; Wang, Jing

2016-06-01

The aim of this study is to predict early distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT) using clinical parameters by machine learning algorithms. The dataset used in this work includes 81 early stage NSCLC patients with at least 6months of follow-up who underwent SBRT between 2006 and 2012 at a single institution. The clinical parameters (n=18) for each patient include demographic parameters, tumor characteristics, treatment fraction schemes, and pretreatment medications. Three predictive models were constructed based on different machine learning algorithms: (1) artificial neural network (ANN), (2) logistic regression (LR) and (3) support vector machine (SVM). Furthermore, to select an optimal clinical parameter set for the model construction, three strategies were adopted: (1) clonal selection algorithm (CSA) based selection strategy; (2) sequential forward selection (SFS) method; and (3) statistical analysis (SA) based strategy. 5-cross-validation is used to validate the performance of each predictive model. The accuracy was assessed by area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity of the system was also evaluated. The AUCs for ANN, LR and SVM were 0.75, 0.73, and 0.80, respectively. The sensitivity values for ANN, LR and SVM were 71.2%, 72.9% and 83.1%, while the specificity values for ANN, LR and SVM were 59.1%, 63.6% and 63.6%, respectively. Meanwhile, the CSA based strategy outperformed SFS and SA in terms of AUC, sensitivity and specificity. Based on clinical parameters, the SVM with the CSA optimal parameter set selection strategy achieves better performance than other strategies for predicting distant failure in lung SBRT patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Fabrication of micro-lens array on convex surface by meaning of micro-milling

NASA Astrophysics Data System (ADS)

Zhang, Peng; Du, Yunlong; Wang, Bo; Shan, Debin

2014-08-01

In order to develop the application of the micro-milling technology, and to fabricate ultra-precision optical surface with complex microstructure, in this paper, the primary experimental research on micro-milling complex microstructure array is carried out. A complex microstructure array surface with vary parameters is designed, and the mathematic model of the surface is set up and simulated. For the fabrication of the designed microstructure array surface, a micro three-axis ultra-precision milling machine tool is developed, aerostatic guideway drove directly by linear motor is adopted in order to guarantee the enough stiffness of the machine, and novel numerical control strategy with linear encoders of 5nm resolution used as the feedback of the control system is employed to ensure the extremely high motion control accuracy. With the help of CAD/CAM technology, convex micro lens array on convex spherical surface with different scales on material of polyvinyl chloride (PVC) and pure copper is fabricated using micro tungsten carbide ball end milling tool based on the ultra-precision micro-milling machine. Excellent nanometer-level micro-movement performance of the axis is proved by motion control experiment. The fabrication is nearly as the same as the design, the characteristic scale of the microstructure is less than 200μm and the accuracy is better than 1μm. It prove that ultra-precision micro-milling technology based on micro ultra-precision machine tool is a suitable and optional method for micro manufacture of microstructure array surface on different kinds of materials, and with the development of micro milling cutter, ultraprecision micro-milling complex microstructure surface will be achieved in future.

Characteristics for electrochemical machining with nanoscale voltage pulses.

PubMed

Lee, E S; Back, S Y; Lee, J T

2009-06-01

Electrochemical machining has traditionally been used in highly specialized fields, such as those of the aerospace and defense industries. It is now increasingly being applied in other industries, where parts with difficult-to-cut material, complex geometry and tribology, and devices of nanoscale and microscale are required. Electric characteristic plays a principal function role in and chemical characteristic plays an assistant function role in electrochemical machining. Therefore, essential parameters in electrochemical machining can be described current density, machining time, inter-electrode gap size, electrolyte, electrode shape etc. Electrochemical machining provides an economical and effective method for machining high strength, high tension and heat-resistant materials into complex shapes such as turbine blades of titanium and aluminum alloys. The application of nanoscale voltage pulses between a tool electrode and a workpiece in an electrochemical environment allows the three-dimensional machining of conducting materials with sub-micrometer precision. In this study, micro probe are developed by electrochemical etching and micro holes are manufactured using these micro probe as tool electrodes. Micro holes and microgroove can be accurately achieved by using nanoscale voltages pulses.

Prediction of the far field noise from wind energy farms

NASA Technical Reports Server (NTRS)

Shepherd, K. P.; Hubbard, H. H.

1986-01-01

The basic physical factors involved in making predictions of wind turbine noise and an approach which allows for differences in the machines, the wind energy farm configurations and propagation conditions are reviewed. Example calculations to illustrate the sensitivity of the radiated noise to such variables as machine size, spacing and numbers, and such atmosphere variables as absorption and wind direction are presented. It is found that calculated far field distances to particular sound level contours are greater for lower values of atmospheric absorption, for a larger total number of machines, for additional rows of machines and for more powerful machines. At short and intermediate distances, higher sound pressure levels are calculated for closer machine spacings, for more powerful machines, for longer row lengths and for closer row spacings.

New Technique of High-Performance Torque Control Developed for Induction Machines

NASA Technical Reports Server (NTRS)

Kenny, Barbara H.

2003-01-01

Two forms of high-performance torque control for motor drives have been described in the literature: field orientation control and direct torque control. Field orientation control has been the method of choice for previous NASA electromechanical actuator research efforts with induction motors. Direct torque control has the potential to offer some advantages over field orientation, including ease of implementation and faster response. However, the most common form of direct torque control is not suitable for the highspeed, low-stator-flux linkage induction machines designed for electromechanical actuators with the presently available sample rates of digital control systems (higher sample rates are required). In addition, this form of direct torque control is not suitable for the addition of a high-frequency carrier signal necessary for the "self-sensing" (sensorless) position estimation technique. This technique enables low- and zero-speed position sensorless operation of the machine. Sensorless operation is desirable to reduce the number of necessary feedback signals and transducers, thus improving the reliability and reducing the mass and volume of the system. This research was directed at developing an alternative form of direct torque control known as a "deadbeat," or inverse model, solution. This form uses pulse-width modulation of the voltage applied to the machine, thus reducing the necessary sample and switching frequency for the high-speed NASA motor. In addition, the structure of the deadbeat form allows the addition of the high-frequency carrier signal so that low- and zero-speed sensorless operation is possible. The new deadbeat solution is based on using the stator and rotor flux as state variables. This choice of state variables leads to a simple graphical representation of the solution as the intersection of a constant torque line with a constant stator flux circle. Previous solutions have been expressed only in complex mathematical terms without a method to clearly visualize the solution. The graphical technique allows a more insightful understanding of the operation of the machine under various conditions.

Optimizing friction stir weld parameters of aluminum and copper using conventional milling machine

NASA Astrophysics Data System (ADS)

Manisegaran, Lohappriya V.; Ahmad, Nurainaa Ayuni; Nazri, Nurnadhirah; Noor, Amirul Syafiq Mohd; Ramachandran, Vignesh; Ismail, Muhammad Tarmizizulfika; Ahmad, Ku Zarina Ku; Daruis, Dian Darina Indah

2018-05-01

The joining of two of any particular materials through friction stir welding (FSW) are done by a rotating tool and the work piece material that generates heat which causes the region near the FSW tool to soften. This in return will mechanically intermix the work pieces. The first objective of this study is to join aluminum plates and copper plates by means of friction stir welding process using self-fabricated tools and conventional milling machine. This study also aims to investigate the optimum process parameters to produce the optimum mechanical properties of the welding joints for Aluminum plates and Copper plates. A suitable tool bit and a fixture is to be fabricated for the welding process. A conventional milling machine will be used to weld the aluminum and copper. The most important parameters to enable the process are speed and pressure of the tool (or tool design and alignment of the tool onto the work piece). The study showed that the best surface finish was produced from speed of 1150 rpm and tool bit tilted to 3°. For a 200mm × 100mm Aluminum 6061 with plate thickness of 2 mm at a speed of 1 mm/s, the time taken to complete the welding is only 200 seconds or equivalent to 3 minutes and 20 seconds. The Copper plates was successfully welded using FSW with tool rotation speed of 500 rpm, 700 rpm, 900 rpm, 1150 rpm and 1440 rpm and with welding traverse rate of 30 mm/min, 60 mm/min and 90 mm/min. As the conclusion, FSW using milling machine can be done on both Aluminum and Copper plates, however the weld parameters are different for the two types of plates.

Sensitivity analysis of machine-learning models of hydrologic time series

NASA Astrophysics Data System (ADS)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B–induced local skin hypersensitization in healthy subjects: a machine-learned analysis

PubMed Central

Lötsch, Jörn; Geisslinger, Gerd; Heinemann, Sarah; Lerch, Florian; Oertel, Bruno G.; Ultsch, Alfred

2018-01-01

Abstract The comprehensive assessment of pain-related human phenotypes requires combinations of nociceptive measures that produce complex high-dimensional data, posing challenges to bioinformatic analysis. In this study, we assessed established experimental models of heat hyperalgesia of the skin, consisting of local ultraviolet-B (UV-B) irradiation or capsaicin application, in 82 healthy subjects using a variety of noxious stimuli. We extended the original heat stimulation by applying cold and mechanical stimuli and assessing the hypersensitization effects with a clinically established quantitative sensory testing (QST) battery (German Research Network on Neuropathic Pain). This study provided a 246 × 10-sized data matrix (82 subjects assessed at baseline, following UV-B application, and following capsaicin application) with respect to 10 QST parameters, which we analyzed using machine-learning techniques. We observed statistically significant effects of the hypersensitization treatments in 9 different QST parameters. Supervised machine-learned analysis implemented as random forests followed by ABC analysis pointed to heat pain thresholds as the most relevantly affected QST parameter. However, decision tree analysis indicated that UV-B additionally modulated sensitivity to cold. Unsupervised machine-learning techniques, implemented as emergent self-organizing maps, hinted at subgroups responding to topical application of capsaicin. The distinction among subgroups was based on sensitivity to pressure pain, which could be attributed to sex differences, with women being more sensitive than men. Thus, while UV-B and capsaicin share a major component of heat pain sensitization, they differ in their effects on QST parameter patterns in healthy subjects, suggesting a lack of redundancy between these models. PMID:28700537

Understanding overlay signatures using machine learning on non-lithography context information

NASA Astrophysics Data System (ADS)

Overcast, Marshall; Mellegaard, Corey; Daniel, David; Habets, Boris; Erley, Georg; Guhlemann, Steffen; Thrun, Xaver; Buhl, Stefan; Tottewitz, Steven

2018-03-01

Overlay errors between two layers can be caused by non-lithography processes. While these errors can be compensated by the run-to-run system, such process and tool signatures are not always stable. In order to monitor the impact of non-lithography context on overlay at regular intervals, a systematic approach is needed. Using various machine learning techniques, significant context parameters that relate to deviating overlay signatures are automatically identified. Once the most influential context parameters are found, a run-to-run simulation is performed to see how much improvement can be obtained. The resulting analysis shows good potential for reducing the influence of hidden context parameters on overlay performance. Non-lithographic contexts are significant contributors, and their automatic detection and classification will enable the overlay roadmap, given the corresponding control capabilities.

Optimization of Maghemite (γ-Fe2O3) Nano-Powder Mixed micro-EDM of CoCrMo with Multiple Responses Using Gray Relational Analysis (GRA)

NASA Astrophysics Data System (ADS)

Mejid Elsiti, Nagwa; Noordin, M. Y.; Idris, Ani; Saed Majeed, Faraj

2017-10-01

This paper presents an optimization of process parameters of Micro-Electrical Discharge Machining (EDM) process with (γ-Fe2O3) nano-powder mixed dielectric using multi-response optimization Grey Relational Analysis (GRA) method instead of single response optimization. These parameters were optimized based on 2-Level factorial design combined with Grey Relational Analysis. The machining parameters such as peak current, gap voltage, and pulse on time were chosen for experimentation. The performance characteristics chosen for this study are material removal rate (MRR), tool wear rate (TWR), Taper and Overcut. Experiments were conducted using electrolyte copper as the tool and CoCrMo as the workpiece. Experimental results have been improved through this approach.

75 FR 71146 - In the Matter of Certain Machine Vision Software, Machine Vision Systems, and Products Containing...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-22

...''); Amistar Automation, Inc. (``Amistar'') of San Marcos, California; Techno Soft Systemnics, Inc. (``Techno..., the ALJ's construction of the claim terms ``test,'' ``match score surface,'' and ``gradient direction...

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

PubMed

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models

PubMed Central

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance. PMID:26926235

Automatic alkaloid removal system.

PubMed

Yahaya, Muhammad Rizuwan; Hj Razali, Mohd Hudzari; Abu Bakar, Che Abdullah; Ismail, Wan Ishak Wan; Muda, Wan Musa Wan; Mat, Nashriyah; Zakaria, Abd

2014-01-01

This alkaloid automated removal machine was developed at Instrumentation Laboratory, Universiti Sultan Zainal Abidin Malaysia that purposely for removing the alkaloid toxicity from Dioscorea hispida (DH) tuber. It is a poisonous plant where scientific study has shown that its tubers contain toxic alkaloid constituents, dioscorine. The tubers can only be consumed after it poisonous is removed. In this experiment, the tubers are needed to blend as powder form before inserting into machine basket. The user is need to push the START button on machine controller for switching the water pump ON by then creating turbulence wave of water in machine tank. The water will stop automatically by triggering the outlet solenoid valve. The powders of tubers are washed for 10 minutes while 1 liter of contaminated water due toxin mixture is flowing out. At this time, the controller will automatically triggered inlet solenoid valve and the new water will flow in machine tank until achieve the desire level that which determined by ultra sonic sensor. This process will repeated for 7 h and the positive result is achieved and shows it significant according to the several parameters of biological character ofpH, temperature, dissolve oxygen, turbidity, conductivity and fish survival rate or time. From that parameter, it also shows the positive result which is near or same with control water and assuming was made that the toxin is fully removed when the pH of DH powder is near with control water. For control water, the pH is about 5.3 while water from this experiment process is 6.0 and before run the machine the pH of contaminated water is about 3.8 which are too acid. This automated machine can save time for removing toxicity from DH compared with a traditional method while less observation of the user.

Normal contour error measurement on-machine and compensation method for polishing complex surface by MRF

NASA Astrophysics Data System (ADS)

Chen, Hua; Chen, Jihong; Wang, Baorui; Zheng, Yongcheng

2016-10-01

The Magnetorheological finishing (MRF) process, based on the dwell time method with the constant normal spacing for flexible polishing, would bring out the normal contour error in the fine polishing complex surface such as aspheric surface. The normal contour error would change the ribbon's shape and removal characteristics of consistency for MRF. Based on continuously scanning the normal spacing between the workpiece and the finder by the laser range finder, the novel method was put forward to measure the normal contour errors while polishing complex surface on the machining track. The normal contour errors was measured dynamically, by which the workpiece's clamping precision, multi-axis machining NC program and the dynamic performance of the MRF machine were achieved for the verification and security check of the MRF process. The unit for measuring the normal contour errors of complex surface on-machine was designed. Based on the measurement unit's results as feedback to adjust the parameters of the feed forward control and the multi-axis machining, the optimized servo control method was presented to compensate the normal contour errors. The experiment for polishing 180mm × 180mm aspherical workpiece of fused silica by MRF was set up to validate the method. The results show that the normal contour error was controlled in less than 10um. And the PV value of the polished surface accuracy was improved from 0.95λ to 0.09λ under the conditions of the same process parameters. The technology in the paper has been being applied in the PKC600-Q1 MRF machine developed by the China Academe of Engineering Physics for engineering application since 2014. It is being used in the national huge optical engineering for processing the ultra-precision optical parts.

Quantification of microscopic surface features of single point diamond turned optics with subsequent chemical polishing

NASA Astrophysics Data System (ADS)

Cardenas, Nelson; Kyrish, Matthew; Taylor, Daniel; Fraelich, Margaret; Lechuga, Oscar; Claytor, Richard; Claytor, Nelson

2015-03-01

Electro-Chemical Polishing is routinely used in the anodizing industry to achieve specular surface finishes of various metals products prior to anodizing. Electro-Chemical polishing functions by leveling the microscopic peaks and valleys of the substrate, thereby increasing specularity and reducing light scattering. The rate of attack is dependent of the physical characteristics (height, depth, and width) of the microscopic structures that constitute the surface finish. To prepare the sample, mechanical polishing such as buffing or grinding is typically required before etching. This type of mechanical polishing produces random microscopic structures at varying depths and widths, thus the electropolishing parameters are determined in an ad hoc basis. Alternatively, single point diamond turning offers excellent repeatability and highly specific control of substrate polishing parameters. While polishing, the diamond tool leaves behind an associated tool mark, which is related to the diamond tool geometry and machining parameters. Machine parameters such as tool cutting depth, speed and step over can be changed in situ, thus providing control of the spatial frequency of the microscopic structures characteristic of the surface topography of the substrate. By combining single point diamond turning with subsequent electro-chemical etching, ultra smooth polishing of both rotationally symmetric and free form mirrors and molds is possible. Additionally, machining parameters can be set to optimize post polishing for increased surface quality and reduced processing times. In this work, we present a study of substrate surface finish based on diamond turning tool mark spatial frequency with subsequent electro-chemical polishing.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Characterization of Machine Variability and Progressive Heat Treatment in Selective Laser Melting of Inconel 718

NASA Technical Reports Server (NTRS)

Prater, T.; Tilson, W.; Jones, Z.

2015-01-01

The absence of an economy of scale in spaceflight hardware makes additive manufacturing an immensely attractive option for propulsion components. As additive manufacturing techniques are increasingly adopted by government and industry to produce propulsion hardware in human-rated systems, significant development efforts are needed to establish these methods as reliable alternatives to conventional subtractive manufacturing. One of the critical challenges facing powder bed fusion techniques in this application is variability between machines used to perform builds. Even with implementation of robust process controls, it is possible for two machines operating at identical parameters with equivalent base materials to produce specimens with slightly different material properties. The machine variability study presented here evaluates 60 specimens of identical geometry built using the same parameters. 30 samples were produced on machine 1 (M1) and the other 30 samples were built on machine 2 (M2). Each of the 30-sample sets were further subdivided into three subsets (with 10 specimens in each subset) to assess the effect of progressive heat treatment on machine variability. The three categories for post-processing were: stress relief, stress relief followed by hot isostatic press (HIP), and stress relief followed by HIP followed by heat treatment per AMS 5664. Each specimen (a round, smooth tensile) was mechanically tested per ASTM E8. Two formal statistical techniques, hypothesis testing for equivalency of means and one-way analysis of variance (ANOVA), were applied to characterize the impact of machine variability and heat treatment on six material properties: tensile stress, yield stress, modulus of elasticity, fracture elongation, and reduction of area. This work represents the type of development effort that is critical as NASA, academia, and the industrial base work collaboratively to establish a path to certification for additively manufactured parts. For future flight programs, NASA and its commercial partners will procure parts from vendors who will use a diverse range of machines to produce parts and, as such, it is essential that the AM community develop a sound understanding of the degree to which machine variability impacts material properties.

Machine Protection with a 700 MJ Beam

NASA Astrophysics Data System (ADS)

Baer, T.; Schmidt, R.; Wenninger, J.; Wollmann, D.; Zerlauth, M.

After the high luminosity upgrade of the LHC, the stored energy per proton beam will increase by a factor of two as compared to the nominal LHC. Therefore, many damage studies need to be revisited to ensure a safe machine operation with the new beam parameters. Furthermore, new accelerator equipment like crab cavities might cause new failure modes, which are not sufficiently covered by the current machine protection system of the LHC. These failure modes have to be carefully studied and mitigated by new protection systems. Finally the ambitious goals for integrated luminosity delivered to the experiments during the era of HL-LHC require an increase of the machine availability without jeopardizing equipment protection.