Multi-response optimization of EDM parameters for DIN 1.2365 steel using Taguchi-grey relational analysis

Purpose This study aims to optimize the machining parameters on the average surface roughness (Ra), metal removal rate (MRR), and overcut (OC) values of DIN 1.2365 (H10) steel during the Electrical Discharge Machining (EDM). Design/methodology/approach The cutting parameters were optimized using multi-response method with Taguchi-based grey relational analysis to determine the optimal machining parameters. Findings Discharge current was identified as the most influential parameter for both Ra and MRR, while pulse duration emerged as the dominant factor affecting OC. Conversely, ANOVA results revealed that discharge current contributed to 80.62 and 82.27% of Ra and MRR, respectively, while pulse duration accounted for 50.16% of the variation in OC. The experimental study achieved confidence levels of approximately 93%, 92%, and 90% for Ra, MRR, and OC, respectively. Practical implications The study provided a predictive analysis of surface quality in EDM machining of DIN 1.2365 steel, enabling the selection of appropriate parameters. Furthermore, by predicting surface quality, it minimized time and material loss, allowing for cost reductions in production. Originality/value This study highlights the use of a multiple optimization method to optimize Ra, MRR, and OC, which are key outputs in EDM machining of DIN 1.2365 steel, commonly used in mold production. The literature generally focuses on similar steels, and it appears that DIN 1.2365 steel, with its superior thermal conductivity, has not received sufficient attention. Furthermore, since studies focusing on a single output in EDM machining of this material and multiple optimization studies are limited, this study makes a significant contribution to the field.