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FE SIMULATION AND experimental validation.OF POWDER MIXED EDM PROCESS FOR ESTIMATING THE TEMPERATURE DISTRIBUTION AND VOLUME REMOVED IN SINGLE CRATER

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International Journal of Modeling, Simulation, and Scientific Computing 2012年第2期3卷 167-188页

作者： ANIRBAN BHATTACHARYA AJAY BATISH KULWINDER SINGH Mechanical Engineering Department Thapar University Patiala–147004India

This study reports the results of a finite element simulation of powder mixed electric discharge machining process for H11 Hot Die steel material using relevant boundary conditions and reasonable *** crater shape was developed using simulated temperature profiles to estimate the volume removed in a single *** temperature distribution on the workpiece was used to predict the cooling rate and calculate the stresses generated due to thermal ***,the simulation results were experimentally validated by physically measuring the crater shape and *** the results it was concluded that about 25%of heat is transmitted to the workpiece during machining at the process conditions used in the *** microscopic pictures showed bigger craters with increase in *** machined surface showed overlapping craters with surface cracks suggesting a high cooling rate.

关键词： Finite element simulation powder mixed electric discharge machining crater cooling rate stress experimental validation.

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New Results on PWARX Model Identification Based on Clustering Approach

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International Journal of Automation and computing 2014年第2期11卷 180-188页

作者： Zeineb Lassoued Kamel Abderrahim Numerical Control of Industrial Processes National School of Engineers of GabesUniversity of GabesSt Omar Ibn-Khattab 6029 Gabes Tunisia

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.

关键词： Hybrid systems piecewise autoregressive systems with exogenous input(PWARX) model clustering identification density-based spatial clustering of applications with noise(DBSCAN) clustering technique experimental validation.

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