Investigation and Optimization on Parameters of Gas Additive Powder Mixed Near Dry –EDM (GAPMNDEDM) by using Taguchi based- Grey Relational Optimization

Abstract

The Gas Additive Powder Mix near Dry Electric Discharge Machining (GAPMND-EDM) serves a manufacturing purpose, specifically for cutting hard materials. This involves the removal of material from a workpiece via fusion, ablation, and evaporation caused by the heat energy generated through electric sparks during energy supply. The process offers several advantages in terms of performance and characteristics. This research project aims to optimize key process parameters, including material removal rate, surface roughness, residual stresses, and microhardness. The study employs a methodology that combines the standard deviation-based objective weighting method with GRA (Gray Relational Analysis) optimization to enhance the hybrid GAPMND-EDM process when applied to EN-31 material. Experimental runs were conducted to evaluate the impact of various input factors, such as pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration. The taguchi-based GRA method was utilized for this purpose, and the experimental design followed an L-27 orthogonal array with the assistance of Minitab-19 software. A total of 27 experiments were performed, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was executed to analyze the influence of pulse-on time, discharge current, dielectric fluid pressure, and metallic powder concentration on Material Removal Rate (MRR), Surface Roughness (SR), Residual Stress (RS), and Microhardness (MH). The result shows the optimal combination of parameters, denoted as A2B2C2D1, was identified as the preferred configuration.