Effect of Electrode Material and Hybrid Metaheuristic Optimization on Die Corner Accuracy during Wire Electrical Discharge Machining on Inconel (625): A Comparative Study

Abstract

Wire electrical discharge machining (WEDM) has the potential to machine intricate shapes of electrically conductive materials with different hardness. The machining task becomes complicated if a V-shaped profile has to be machined. This corner-cutting process outcome offers a die corner error (CE). It may affect the die and punch alignment. The benefits of zinc-coated copper wire “(BroncoCut-X)” (Wire1) and uncoated brass wire (Wire2) for machining Inconel(625) using WEDM have been reported in this study. The experiments have been conducted at different levels of pulse-on time (S_on), wire tension (WT), flushing pressure (FP), and discharge current (I_d). The machining outcomes of CE, surface roughness (RA), and material removal rate (MRR) with the variation of said wires have been collected and analyzed using main effect plots, scanning electron microscope (SEM), and analysis of variance (ANOVA) analysis. The comparison result revealed that Wire1 improves from its counterpart by 29.6, 8.4, and 46.5% of CE, RA and MRR, respectively. Moreover, a hybrid parametric optimization is selected with the combination of "Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)” with "Fuzzy-Interference System (FIS)” and JAYA algorithm (JA). The MOORA and FIS are used to convert machining responses into a single objective (terms as MOORA-fuzzy reasoning grade (MFRG)). The optimal performance is calculated using JA, and the MFRG model is considered for the fitness function. The result was compared with the teaching learning-based optimization (TLBO) to check the efficacy of the approach. The confirmatory tests recorded 8.37 and 5.50% overall machining improvements for Wire1 and Wire2, respectively, with the proposed hybrid optimization technique.