Multi-response optimisation of electrical discharge machining process using combined approach of RSM and FIS

In this work, an integrated approach for optimising multiple responses using response surface methodology (RSM) combined with fuzzy inference system (FIS) and particle swarm optimisation (PSO) algorithm is proposed for obtaining best parametric combination in electrical discharge machining (EDM) process. Four process parameters (factors) such as discharge current (Ip), pulse-on-time (Ton), duty factor (τ) and flushing pressure (Fp) and four important responses like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra) and circularity (r1/r2) of machined component are considered in this study. As the responses are conflicting in nature, a single combination of parameters cannot produce best performance satisfying all the responses. A Box-Behnken design is used to generate data from experimental set up and Mamdani-type fuzzy inference system is used to convert multiple responses into a single equivalent characteristic index known as multi-response performance characteristic index (MPCI). The relationship between MPCI and process parameters is expressed mathematically using multiple regression analysis. Finally, particle swarm optimisation technique is employed to obtain best parametric combination that maximises MPCI. The machined surfaces are analysed through scanning electron microscope (SEM) to study the effect of statistically significant parameters on pores and micro crack found in the of work piece material.