Intelligent Modeling and Multiobjective Optimization of Die Sinking Electrochemical Spark Machining Process

Abstract

Die sinking–electrochemical spark machining (DS–ECSM) is one of the hybrid machining processes, combining the features of electrochemical machining (ECM) and electro-discharge machining (EDM), used for machining of nonconducting materials. This article reports an intelligent approach for the modelling of DS–ECSM process using finite element method (FEM) and artificial neural network (ANN) in integrated manner. It primarily comprises development of two models. The first one is the development of a thermal finite element model to estimate the temperature distribution within the heat-affected zone (HAZ) of single spark on the workpiece during DS–ECSM. The estimated temperature field is further post-processed for determination of material removal rate (MRR) and average surface roughness (ASR). The second one is a back propagation neural network (BPNN)-based process model used in a simulation study to find optimal machining parameters. The BPNN model has been trained and tested using the data generated from the FEM simulations. The trained neural network system has been used in predicting MRR and ASR for different input conditions. The ANN model is found to accurately predict DS–ECSM process responses for chosen process conditions. This article also presents an effective approach for multiobjective optimization of DS–ECSM process using grey relational analysis.

Keywords:

• Artificial neural network (ANN)
• Average surface roughness (ASR)
• Electrochemical spark machining (ECSM)
• Finite element method (FEM)
• Grey relational analysis (GRA)
• Material removal rate (MRR)

Notes

*Optimum level.