OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 which proved the optimization method’s reliability. The combination of high MRR and low SR and TWR shows that this EDM enables efficient high-quality machining suitable for use in automotive and aerospace components with demanding performance requirements. Conclusions The research offers a thorough assessment of the impact of process parameters on the utility function that combines MRR, SR, and TWR in EDM. The experiments conducted in Trials 15 and 9 yielded MRR rates of 9.076 mm³/min and 8.995 mm³/min, together with S/N ratios measuring 19.1572 dB and 19.0883 dB, respectively. The machining parameters used in Trial 1 along with Trial 16 yielded the lowest MRR values of 2.096 mm³/min and 2.805 mm³/min, indicating insufficient material removal. The surface finish in Trial 1 achieved ideal effect due to its lowest roughness values (SR1 = 2.238 µm, SR2 = 2.244 µm, SR3 = 2.242 µm) and the highest S/N ratio (−7.0101 dB). Trial 3 demonstrated poor surface quality with high roughness values (SR1 = 3.704 µm, SR2 = 3.716 µm, and SR3 = 3.712 µm) due to excessive tool wear and the highest discharge energy, and also showed a low S/N ratio of −11.3890 dB. Trial 10 showed the best performance due to the minimum TWR of 0.041 mm³/min along with the best S/N ratio of 27.6633 dB. The Taguchi-based optimization method has proven to be effective in finding the optimal machining conditions that provide the highest MRR performance along with the lowest SR and TWR results. ANOVA statistics confirm that pulse-on time, pulse-off time, and current play an important role in influencing the performance results during machining operations. The method shows the importance of precise control of EDM parameters to achieve the best machining results. Each particular response (MRR, SR, and TWR) required individual optimal values for the machining parameters from the research findings. When dealing with multiple optimization objectives, utility theory helped develop a satisfactory compromise that provides uniform performance in each response area. The higher current level increases the material removal rate, but also results in a slight increase in surface roughness and tool wear. The main determinant of machining performance accounted for 84.102 % of the total utility function. The quality of the machining process performance improves when using lower voltage gaps, as this creates more reliable sparks. The surface quality remains high, as both material removal and pulse-on time increase together. The performance results are primarily determined by the pulse-off time, as this parameter contributes only 3.089 % to the overall score. The quality of the performance and the S/N ratio are best improved when the level is set to 4219. The results show that the gap voltage (Vg) is the main influencing factor followed by the discharge current (Ig), while the pulse-off time (Toff) has no significant effect. A total of 92.5 % of the data variability (R²) can be explained by the model, proving its high reliability in predicting the optimal machining scenarios. Trial number 17 achieved the optimal S/N ratio of 13.098 dB, which resulted in the best experimental results, thus becoming the optimal parameter setting among all the tested trials. In contrast, trial number 16 had the poorest experimental performance. The better machining quality exists as a direct consequence of the increased S/N ratios, demonstrating the success of the optimization strategy. Optimizing the gap voltage is essential to achieving optimal EDM performance results. Increasing the discharge current accelerates material removal, but users need to manage it to prevent excessive tool degradation. The pulse-off time has little effect on operational efficiency, although shorter intervals improve performance. The utility approach applies various performance metrics to determine the optimal machining process that provides the highest efficiency and quality results. The gap voltage (Vg) process parameter showed the greatest impact on the utility function, as its delta values reached 4.026 for the S/N data, while reaching 1.549 for the raw data. The ANOVA analysis yielded a P-value of 0.000, which was statistically significant, and the contribution rate to the results was 85.98 %. The impact of the gap current (Ig) on the utility function was moderate based on its delta values of 1.022 (S/N data) and 0.365 (raw data), resulting in a contribution of 4.76% according to ANOVA. The pulse-off time (Toff) was found to have the least impact on the utility function performance
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