A Novel Method for Selecting the Optimal Edm Process for Hastelloy B2 Using the Modified-Additive Ratio Assessment Method (M-Aras) Based Adaptive Neuro Fuzzy Inference System (Anfis)

Abstract

Electrode wear and metal removal exhibited nonlinear behavior in the Electrical Discharge Machining (EDM) of Hastelloy B2 plate. Hence, mathematical modeling was used to solve this problem. The hole size, pulse duration, duty cycle, and current were selected as inputs. Squareness and taper angle were considered as responses. Therefore, the Modified-Additive Ratio Assessment Method (M-ARAS) based Adaptive Neuro Fuzzy Inference System (ANFIS) method was used to find the optimum EDM process parameters. The overall analysis showed that the M-ARAS-based ANFIS algorithm provided a good fit for optimization of the process parameters and could be used for further multi-objective optimization problems.