Research Article
Comparative Study on EDM Parameter Optimization for Adsorbed Si3N4–TiN using TOPSIS and GRA Coupled with TLBO Algorithm
Table 11
Candidate solution based on the nondominance rank.
| S. no | Combined input parameter | New response | GRG | Rank | I | PON | POFF | DP | SV | MRR | EWR | SR | ROC | θ | CIR | CYL |
| 1 | 12 | 7 | 2.7 | 9 | 36 | 0.00658 | 0.00058 | 1.36 | 0.19 | 0.0014 | 0.0667 | 0.0321 | 0.75302 | 1 | 2 | 12 | 7 | 4 | 12 | 36 | 0.00219 | 0.00058 | 0.77 | 0.15 | 0.1098 | 0.0309 | 0.0120 | 0.74252 | 2 | 3 | 10.9 | 8.55 | 2.7 | 20 | 32 | 0.00573 | 0.00079 | 0.79 | 0.13 | 0.0610 | 0.0361 | 0.0211 | 0.73003 | 3 | 4 | 12 | 9 | 9.3 | 16 | 28.3 | 0.00610 | 0.00065 | 2.70 | 0.17 | 0.1506 | 0.0101 | 0.0186 | 0.72975 | 4 | 5 | 10.9 | 7 | 2 | 19.5 | 28.3 | 0.00542 | 0.00010 | 0.26 | 0.18 | 0.0092 | 0.0399 | 0.0436 | 0.72284 | 5 | 6 | 6 | 6 | 6 | 18 | 36 | 0.00280 | 0.00073 | 1.51 | 0.15 | 0.5622 | 0.0190 | 0.0094 | 0.72120 | 6 | 7 | 12 | 5.45 | 2 | 20 | 35.7 | 0.00530 | 0.00059 | 0.34 | 0.14 | 0.0341 | 0.0523 | 0.0265 | 0.71295 | 7 | 8 | 8 | 8.55 | 2 | 16 | 36 | 0.00502 | 0.00083 | 1.12 | 0.16 | 0.0898 | 0.0646 | 0.0339 | 0.70670 | 8 | 9 | 10.9 | 9 | 6 | 9 | 35.7 | 0.00648 | 0.00062 | 2.56 | 0.18 | 0.0993 | 0.0515 | 0.0207 | 0.70244 | 9 | 10 | 10.9 | 3.9 | 9.3 | 12.5 | 36 | 0.00450 | 0.00029 | 2.66 | 0.21 | 0.1647 | 0.0543 | 0.0435 | 0.69953 | 10 | 11 | 12 | 3.9 | 10 | 19.5 | 32 | 0.00430 | 0.00053 | 2.24 | 0.18 | 0.1789 | 0.0270 | 0.0359 | 0.69858 | 11 | 12 | 4 | 5.45 | 2.7 | 12.5 | 28.3 | 0.00295 | 0.00080 | 0.93 | 0.29 | 0.0750 | 0.0858 | 0.1034 | 0.69752 | 12 | 13 | 12 | 8.55 | 6 | 12.5 | 28 | 0.00652 | 0.00040 | 1.91 | 0.20 | 0.0514 | 0.0297 | 0.0332 | 0.69403 | 13 | 14 | 5.1 | 5.45 | 6 | 19.5 | 36 | 0.00248 | 0.00085 | 1.78 | 0.19 | 0.2230 | 0.0682 | 0.0594 | 0.69307 | 14 | 15 | 10.9 | 5.45 | 10 | 16 | 28 | 0.00464 | 0.00073 | 2.48 | 0.21 | 0.1587 | 0.0260 | 0.0485 | 0.69092 | 15 | 16 | 10 | 7 | 2 | 18 | 30 | 0.00255 | 0.00123 | 1.15 | 0.12 | 0.0877 | 0.0303 | 0.0410 | 0.68945 | 16 | 17 | 12 | 6 | 2 | 20 | 34 | 0.00093 | 0.79000 | 0.05 | 0.06 | 0.0486 | 0.0557 | 0.0313 | 0.68706 | 17 | 18 | 4 | 8.55 | 9.3 | 19.5 | 35.7 | 0.00268 | 0.00116 | 3.12 | 0.16 | 0.3304 | 0.0486 | 0.0406 | 0.68421 | 18 | 19 | 6 | 5 | 4 | 16 | 34 | 0.02139 | 0.00065 | 0.87 | 0.38 | 0.0612 | 0.1752 | 0.1549 | 0.68172 | 19 | 20 | 10 | 8 | 4 | 20 | 32 | 0.00207 | 0.00073 | 1.17 | 0.18 | 0.0951 | 0.0168 | 0.0277 | 0.67799 | 20 | 21 | 4 | 6 | 4 | 14 | 30 | 0.00240 | 0.00032 | 0.71 | 0.24 | 0.0810 | 0.0491 | 0.0299 | 0.67612 | 21 | 22 | 12 | 5 | 10 | 18 | 32 | 0.00117 | 0.00060 | 0.93 | 0.21 | 0.0244 | 0.0778 | 0.0307 | 0.67015 | 22 | 23 | 4 | 8 | 8 | 18 | 34 | 0.00444 | 0.00111 | 0.56 | 0.18 | 0.0848 | 0.0525 | 0.0685 | 0.66312 | 23 | 24 | 6 | 9 | 2 | 14 | 32 | 0.00192 | 0.00069 | 0.92 | 0.16 | 0.0550 | 0.1465 | 0.0520 | 0.66300 | 24 | 25 | 12 | 8 | 6 | 14 | 28 | 0.00267 | 0.00040 | 1.42 | 0.24 | 0.2073 | 0.0137 | 0.0231 | 0.65933 | 25 |
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