Multi-parametric Optimization of Wired Electrical Discharge Machining Process to Minimize Damage Cause in Steel - A Soft Computing based Taguchi-Grey Relation Analysis Approach
Abstract
The automotive industry makes crucial components from a wide array of materials. EN31 steel is a highly regarded engineering functional material that meets the industry's requirements. However, conventional machining is not cost effective due to EN31's high hardness and strength. Thus, the current research focuses on the effect of wired electrical discharge machining (WEDM) process parameters on the material removal rate (MRR) and average arithmatic mean of surface roughness (Ra) of EN-31 steel, as WEDM is a highly sustainable and cost-effective alternative to the conventional machining processes. Experiments are conducted utilizing a Taguchi L27 orthogonal array with the following input parameters: servo voltage, pulse width, pulse interval, and cutting speed. Grey relational analysis (GRA) has been used to optimize the multiple responses. The analysis of variance (ANOVA) of the grey relational grade (GRG) demonstrated that the most influential element in simultaneously improving performance measures is the speed, S (rpm) as it contributes 62.39 % to the variance in the response.
