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Intelligent process modeling and optimization of die-sinking electric discharge machining

DSpace at IIT Bombay

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Title Intelligent process modeling and optimization of die-sinking electric discharge machining
 
Creator JOSHI, SN
PANDE, SS
 
Subject NEURAL-NETWORK MODELS
MATERIAL REMOVAL RATE
PROCESS PARAMETERS
SURFACE-ROUGHNESS
GENETIC ALGORITHM
EDM
PREDICTION
SELECTION
FINISH
Electric discharge machining (EDM)
Process modeling and optimization
Finite element method (FEM)
Artificial neural networks (ANN)
Scaled conjugate gradient algorithm (SCG)
Non-dominated sorting genetic algorithm (NSGA)
 
Description This paper reports an intelligent approach for process modeling and optimization of electric discharge machining (EDM). Physics based process modeling using finite element method (FEM) has been integrated with the soft computing techniques like artificial neural networks (ANN) and genetic algorithm (GA) to improve prediction accuracy of the model with less dependency on the experimental data. A two-dimensional axi-symmetric numerical (FEM) model of single spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time and energy dependent spark radius, etc. to predict the shape of crater, material removal rate (MRR) and tool wear rate (TWR). The model is validated using the reported analytical and experimental results. A comprehensive ANN based process model is proposed to establish relation between input process conditions (current, discharge voltage, duty cycle and discharge duration) and the process responses (crater size, MRR and TWR). The ANN model was trained, tested and tuned by using the data generated from the numerical (FEM) model. It was found to accurately predict EDM process responses for chosen process conditions. The developed ANN process model was used in conjunction with the evolutionary non-dominated sorting genetic algorithm II (NSGA-II) to select optimal process parameters for roughing and finishing operations of EDM. Experimental studies were carried out to verify the process performance for the optimum machining conditions suggested by our approach. The proposed integrated (FEM-ANN-GA) approach was found efficient and robust as the suggested optimum process parameters were found to give the expected optimum performance of the EDM process. (C) 2010 Elsevier B.V. All rights reserved.
 
Publisher ELSEVIER SCIENCE BV
 
Date 2012-06-26T06:14:37Z
2012-06-26T06:14:37Z
2011
 
Type Article
 
Identifier APPLIED SOFT COMPUTING,11(2)2743-2755
1568-4946
http://dx.doi.org/10.1016/j.asoc.2010.11.005
http://dspace.library.iitb.ac.in/jspui/handle/100/14020
 
Language English