Multi-objective optimisation of machining parameters in wire electrical discharge machine using non-dominating sorting genetic algorithm

The present work is aimed at optimising the cutting rate, surface roughness and dimensional deviation of EN‐31 steel considering the simultaneous effect of various input parameters such as pulse on time, pulse off time, wire tension, spark gap set voltage and servo feed. Response surface methodology (RSM) is adopted to study the effect of independent variables on responses and develop predictive models. It is desired to obtain optimal parameter setting that can decrease surface roughness and dimensional deviation while increasing cutting rate. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying all the objectives in one solution. Therefore, it is essential to explore the optimisation landscape to generate the set of dominant solutions. Non‐sorted genetic algorithm (NSGA) has been adopted to optimise the responses such that a set of mutually dominant solutions can be found out.