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

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...

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Vydáno v:	Applied soft computing Ročník 11; číslo 2; s. 2743 - 2755
Hlavní autoři:	Joshi, S.N., Pande, S.S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.03.2011
Témata:	Artificial neural networks (ANN) Electric discharge machining (EDM) Finite element method (FEM) Non-dominated sorting genetic algorithm (NSGA) Process modeling and optimization Scaled conjugate gradient algorithm (SCG) Finite element method (FEM) Electric discharge machining (EDM) Non-dominated sorting genetic algorithm (NSGA) Scaled conjugate gradient algorithm (SCG) Artificial neural networks (ANN) Process modeling and optimization
ISSN:	1568-4946, 1872-9681
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Shrnutí:	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.
ISSN:	1568-4946 1872-9681
DOI:	10.1016/j.asoc.2010.11.005