Multi objective optimisation of SWCNT-based electrical discharge machining process using grey relational and fuzzy logic analysis

Abstract

The multi response optimisation of process parameters like surface roughness (SR) and metal removal rate (MRR) of electrical discharge machining (EDM) of carbon nano tube (CNT) mixed dielectric fluid using grey relational analysis are performed. Experiments are performed with designated EDM conditions obtained using an L9 orthogonal array. Based on the results of the grey relational analysis a set of optimum EDM parameters is obtained. The analysis of variance (ANOVA) approach is used to find the significant parameters influencing the EDM machining process. The results show that the optimum machining conditions determined using grey relational analysis are much better than the original preliminary set of experimental conditions. Atomic force microscope (AFM) is used to analysis the surface characteristics such as surface roughness, micro cracks and topography of CNT-based machining surface. The paper describes the application of a fuzzy logic system to investigate precision and accuracy of EDM process parameters for determining the efficiency of each parameter influencing the machining for with and without using CNT mixed dielectric fluid.