Two-step optimization of electric discharge machining using neural network based approach and TOPSIS

Abstract

The process performance of Electric Discharge Machining (EDM) process directly depends on the combination of input parameters. In this work, a two-step optimization has been performed to find the optimal combination of process parameters. In the first step, a neural network based multi-objective optimization technique is proposed and employed. The proposed approach generates the total possible combinations of input parameters taking the number and levels of each parameter into account. The performance measures for each combination is obtained by a trained neural network model of EDM process. Then, the optimal solution(s) are found following the concept of dominance. By employing the proposed approach, 24 non-dominated combinations of input parameters are obtained in this work. In the second step of optimization, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been employed to award rank to each non-dominated solutions. Prior to that, experiments have been designed by Taguchi’s L36 orthogonal array and the experimental data has been used to train Radial Basis Function Neural Network (RBFNN) model.