OBRABOTKAMETALLOV technology Vol. 27 No. 2 2025 µm to 3.716 µm, showing significant variation in surface finish across different trials. The S/N ratio varies between −7.0101 dB and −11.3890 dB, confirming that process parameters significantly influence surface quality. Trials with closely matched SR1, SR2, and SR3 values indicate high repeatability and process stability (Trial 11: SR1 = 2.648 µm, SR2 = 2.654 µm, SR3 = 2.652 µm). The utility value for raw data was calculated using the equation and is mentioned in the table 5. The machining life duration, together with operational expenses, depends heavily on TWR. S/N ratios for TWR were determined from three repetition readings (TWR1, TWR2, TWR3) in each trial. The tool wear recorded was 0.041 mm³/min in Trial 10, which also achieved the highest S/N ratio of 27.6633 dB, indicating reliable machining performance. Tool life conditions for Trial 6 were found unfavourable because it experienced the most wear (0.239 mm³/min) alongside the lowest S/N ratio (12.4555 dB). The wide variation of TWR measurements between trials demonstrates that tool wear mainly depends on process variables, which include pulse-on time and pulse-off time, alongside current settings. Multi-objective Optimization of Performance Measures Taguchi’s approach identifies the optimal levels of input variables to maximize a single response. However, these input variable settings may lead to adverse outcomes for other responses. Consequently, there is a need to determine an ideal configuration of process variables that provides near-optimal quality attributes across multiple criteria. Taguchi’s approach combined with the utility concept was employed to determine the optimal levels of process variables for multi-objective optimization. Optimal configurations of process variables and ideal values for specific performance measures are shown in Table 6. Based on the Taguchi optimization, the best set of machining parameters for different responses was identified. The maximum MRR was obtained with Trial 15, which had the optimal parameters of A5B3C1D2E3, and the predicted optimal value of MRR is 9.767 mm³/min. The highest MRR is achieved with a combination of high gap current and moderate pulse-on and pulse-off time, ensuring efficient material removal. Ta b l e 6 Optimal settings of process parameters and predicted optimal value of response Responses Trail No. Optimal set of process Variables Predicted optimal response value Maximum MRR 15 A5B3C1D2E3 9.767 mm3/min Minimum SR 1 A1B1C1D1E1 2.2119 µm Minimum TWR 13 A5B1C2D3E1 0.00404 mm3/min The minimum SR was obtained with Trial 1, with the optimal parameters of A1B1C1D1E1, and the predicted optimal surface roughness value is 2.2119 µm. The lowest roughness is obtained using the lowest gap current and the shortest pulse-on time, reducing surface damage and improving finish quality. The optimum tool wear rate (TWR) of the EDM process is achieved at Trial 13, with the optimal parameters of A5B1C2D3E1. The predicted optimal value for the TWR is 0.00404 mm³/min. The lowest tool wear is achieved by optimizing the discharge energy and duty cycle, ensuring minimal electrode erosion. The preference scale is calculated by Equation (11) for MRR (PMRR), Equation (12) for SR (PSR), and Equation (13) for TWR (PTWR). When calculating the preference scale, the predicted values of the optimal responses are as follows: 9.767 mm³/min for MRR, 2.2119 µm for SR, and 0.00404 mm³/min for TWR. The experimental findings indicate that the minimum and maximum MRR range from 2.078 mm³/min to 9.081 mm³/min, the SR ranges from 2.238 µm to 3.716 µm, and the TWR ranges from 0.039 mm³/min to 0.262 mm³/min. 13.03 log ; 2 i MRR x P (11)
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