Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters

Abstract

The process parameters of electric discharge machining such as current, pulse-on time and pulse-off time play a major role for deciding the machining performance such as material removal rate and wear ratio. In this article, the process parameters of electric discharge machining have been optimized for maximum material removal rate and minimum wear ratio. A properly trained neural network has been used to establish the relation between the process parameters and machining performance. Three different evolutionary algorithms such as simulated annealing, genetic algorithm and particle swarm optimization were then used with the neural network model to predict the optimum process parameters for maximum material removal rate and minimum wear ratio. The evolutionary algorithms thus used have been compared in terms of performance.