Experimental analysis and regression modeling of EDM performance parameters for SK2MCr4 steel using multi-variable interaction effects

Abstract

This study investigates the impact of key electrical discharge machining (EDM) parameters: specifically, current, pulseon time, and servo voltage on the material removal rate (MRR) for SK2MCr4 steel. A comprehensive experimental approach employing a full factorial design was utilized to explore both the individual and interaction effects of these parameters. To enhance efficiency and precision, the Taguchi method, in conjunction with ANOVA, was applied for process optimization. The findings revealed that electric current is the most significant factor, accounting for approximately 64–73% of the variance in MRR, followed by pulse on time and servo voltage. The optimal parameters identified include a current of 12 A, a pulse on time of 60 µs, and a servo voltage of 3 V, which resulted in the highest MRR and robust process performance. The regression models demonstrated substantial predictability with R² values exceeding 89%, thereby affirming the importance of the selected factors. Interaction analysis highlighted synergistic effects between current and pulse on time, emphasizing the necessity of multi-variable optimization. Furthermore, the Taguchi approach was found to be more resource efficient than the full factorial design, producing results characterized by lower error margins. The outcomes provide practical recommendations for achieving enhanced productivity and process stability in the EDM of hardened steels, along with insights into the interaction mechanisms affecting MRR. Future research should focus on multi-objective optimization that includes tool wear and surface integrity, as well as validation under industrial conditions to ascertain real world applicability.