(BQ) 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. | Applied Soft Computing 11 (2011) 2743–2755 Contents lists available at ScienceDirect Applied Soft Computing journal homepage: Intelligent process modeling and optimization of die-sinking electric discharge machining AISI P20 mold steel . Joshi a , . Pande b,∗ a b Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India a r t i c l e i n f o Article history: Received 31 January 2009 Received in revised form 11 June 2010 Accepted 17 November 2010 Available online 24 November 2010 Keywords: 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) a b s t r a c t 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
