Modelling and multi-objective optimization of process parameters of wire electrical discharge machining using non-dominated sorting genetic algorithm-II

Abstract

The present article is the first study on and identifies the different process parameters that affect the cutting speed and surface roughness in wire electric discharge machining of titanium 6-2-4-2. Box–Behnken designs are used to plan and analyse the experiments. Mathematical models are developed for cutting speed and surface roughness using regression technique and are utilized for simultaneous optimization of cutting speed and surface roughness. As the influence of process parameters on cutting speed and surface roughness is opposite, the problem is formulated as a multi-objective optimization problem. Non-dominated sorting genetic algorithm-II is then applied to obtain Pareto optimal set of solutions. Confirmatory experiments indicate that the model is suitable for predicting the response parameters. These Pareto optimal sets of solutions can be used as guidelines by manufacturing engineers to select optimal combination of parameters depending upon the job requirements.