Application of ANFIS and GRA for multi-objective optimization of optimal wire-EDM parameters while machining Ti–6Al–4V alloy

The applications of artificial intelligence (AI) mainly, the hybrid approaches are becoming more popular and the relevant researches have been conducted in every field of engineering and science by using these AI techniques. Therefore, this research aims to examine the influence of wire electric-discharge machining parameters on performance parameters to improve the productivity with a higher surface finish of Titanium alloy (Ti–6Al–4V) by using the artificial intelligent technique. In this experimental analysis, the adaptive network based fuzzy inference system (ANFIS) model has been highly-developed and the multi-parametric optimization has been done to find the optimal solution for the machining of titanium superalloy. The peak current (I p ), taper angle, pulse on time (T on ), pulse of time (T off ) and the dielectric fluid flow rate had selected as operation constraints to conduct experimental trials. The surface roughness and MRR were considered as output responses. The influence on machining performance has been analyzed by the ANFIS model and the developed model was validated with the full factorial regression models. The developed models showed the minimum mean percentage error and the optimized parameters by the GRA method showed the considerable improvement in the process.