Multi-objective parametric optimization of powder mixed electro-discharge machining using response surface methodology and non-dominated sorting genetic algorithm

Abstract

Powder mixed electro-discharge machining (EDM) is being widely used in modern metal working industry for producing complex cavities in dies and moulds which are otherwise difficult to create by conventional machining route. It has been experimentally demonstrated that the presence of suspended particle in dielectric fluid significantly increases the surface finish and machining efficiency of EDM process. Concentration of powder (silicon) in the dielectric fluid, pulse on time, duty cycle, and peak current are taken as independent variables on which the machining performance was analysed in terms of material removal rate (MRR) and surface roughness (SR). Experiments have been conducted on an EZNC fuzzy logic Die Sinking EDM machine manufactured by Electronica Machine Tools Ltd. India. A copper electrode having diameter of 25 mm is used to cut EN 31 steel for one hour in each trial. 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 aims at decreasing surface roughness along with larger material removal rate. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying both the objectives in any one solution. Therefore, it is essential to explore the optimization landscape to generate the set of dominant solutions. Non-sorted genetic algorithm (NSGA) has been adopted to optimize the responses such that a set of mutually dominant solutions are found over a wide range of machining parameters.