OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology Ta b l e 4 Mixed L18 (61 × 34) orthogonal array Trail No. Parameter A B C D E 1 1 1 1 1 1 2 1 2 2 2 2 3 1 3 3 3 3 4 2 1 1 2 2 5 2 2 2 3 3 6 2 3 3 1 1 7 3 1 2 1 3 8 3 2 3 2 1 9 3 3 1 3 2 10 4 1 3 3 2 11 4 2 1 1 3 12 4 3 2 2 1 13 5 1 2 3 1 14 5 2 3 1 2 15 5 3 1 2 3 16 6 1 3 2 3 17 6 2 1 3 1 18 6 3 2 1 2 parameters on the output characteristics. The significant and non-significant parameters of EDM process were identified by ANOVA. Statistical data processing was performed using MINITAB 15.0 software. The main effects plot visually displays the influence of each process parameter on the output characteristics, allowing for the assessment of trend changes. The response plot shows the change in the value of the output parameter as a function of the change in the level of the input parameter. The experimental program was executed three times for each parameter combination, after which data were collected. The analysis included both raw data analysis and S/N data analysis to determine the significance of the process parameters by comparing the main effects plots constructed based on S/N data and raw data. Utility theory Optimization based on utility theory allows for the quantitative assessment of product value, considering it as a combination of utility levels corresponding to different quality characteristics. The product optimization problem is reduced to maximizing overall utility by optimizing the individual utility of each characteristic. The first step is to determine the optimal levels of the process parameters using the Taguchi method, which helps improve performance indicators. Then, a preference scale is established for each response (MRR, SR, TWR), taking into account the optimal and minimum values obtained during the experiments. The preference scale is constructed based on the following equation (6): ' log i i i x P A x (6)
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