Research Article
Comparative Study on EDM Parameter Optimization for Adsorbed Si3N4–TiN using TOPSIS and GRA Coupled with TLBO Algorithm
Table 8
Initial random population.
| Run | Input parameter | Response | GRG | Rank | I | PON | POFF | DP | SV | MRR | EWR | SR | ROC | θ | CIR | CYL |
| 1 | 4 | 5 | 2 | 12 | 28 | 0.00298 | 0.00098 | 1.44 | 0.30 | 0.0488 | 0.0744 | 0.1282 | 0.67019 | 15 | 2 | 4 | 6 | 4 | 14 | 30 | 0.00240 | 0.00032 | 0.71 | 0.24 | 0.0810 | 0.0491 | 0.0299 | 0.69752 | 10 | 3 | 4 | 7 | 6 | 16 | 32 | 0.00172 | 0.00053 | 2.53 | 0.23 | 0.0985 | 0.0436 | 0.0545 | 0.63194 | 22 | 4 | 4 | 8 | 8 | 18 | 34 | 0.00444 | 0.00111 | 0.56 | 0.18 | 0.0848 | 0.0525 | 0.0685 | 0.68421 | 14 | 5 | 4 | 9 | 10 | 20 | 36 | 0.00220 | 0.00058 | 4.80 | 0.15 | 0.3140 | 0.0260 | 0.0272 | 0.63471 | 21 | 6 | 6 | 5 | 4 | 16 | 34 | 0.02139 | 0.00065 | 0.87 | 0.38 | 0.0612 | 0.1752 | 0.1549 | 0.65895 | 16 | 7 | 6 | 6 | 6 | 18 | 36 | 0.00280 | 0.00073 | 1.51 | 0.15 | 0.5622 | 0.0190 | 0.0094 | 0.69307 | 12 | 8 | 6 | 7 | 8 | 20 | 28 | 0.00275 | 0.00083 | 2.87 | 0.19 | 0.3540 | 0.0376 | 0.0392 | 0.61013 | 23 | 9 | 6 | 8 | 10 | 12 | 30 | 0.00409 | 0.00080 | 1.69 | 0.20 | 0.3667 | 0.0441 | 0.0748 | 0.59570 | 24 | 10 | 6 | 9 | 2 | 14 | 32 | 0.00192 | 0.00069 | 0.92 | 0.16 | 0.0550 | 0.1465 | 0.0520 | 0.65439 | 17 | 11 | 8 | 5 | 6 | 20 | 30 | 0.00198 | 0.00085 | 1.38 | 0.19 | 0.1229 | 0.0429 | 0.0763 | 0.64286 | 20 | 12 | 8 | 6 | 8 | 12 | 32 | 0.00256 | 0.00079 | 3.99 | 0.20 | 0.0611 | 0.0320 | 0.0496 | 0.65259 | 18 | 13 | 8 | 7 | 10 | 14 | 34 | 0.00204 | 0.00010 | 3.83 | 0.17 | 0.5150 | 0.1121 | 0.0973 | 0.51059 | 25 | 14 | 8 | 8 | 2 | 16 | 36 | 0.00281 | 0.00116 | 1.27 | 0.21 | 0.0268 | 0.0509 | 0.0313 | 0.70670 | 6 | 15 | 8 | 9 | 4 | 18 | 28 | 0.00312 | 0.00145 | 0.69 | 0.23 | 0.0893 | 0.0573 | 0.0818 | 0.65215 | 19 | 16 | 10 | 5 | 8 | 14 | 36 | 0.00199 | 0.00059 | 3.26 | 0.20 | 0.0761 | 0.0205 | 0.0203 | 0.69953 | 8 | 17 | 10 | 6 | 10 | 16 | 28 | 0.00151 | 0.00062 | 2.60 | 0.20 | 0.0871 | 0.0183 | 0.0320 | 0.69092 | 13 | 18 | 10 | 7 | 2 | 18 | 30 | 0.00255 | 0.00123 | 1.15 | 0.12 | 0.0877 | 0.0303 | 0.0410 | 0.72284 | 4 | 19 | 10 | 8 | 4 | 20 | 32 | 0.00207 | 0.00073 | 1.17 | 0.18 | 0.0951 | 0.0168 | 0.0277 | 0.73003 | 2 | 20 | 10 | 9 | 6 | 12 | 34 | 0.00183 | 0.00029 | 2.52 | 0.19 | 0.0210 | 0.0473 | 0.0230 | 0.70244 | 7 | 21 | 12 | 5 | 10 | 18 | 32 | 0.00117 | 0.00060 | 0.93 | 0.21 | 0.0244 | 0.0778 | 0.0307 | 0.69858 | 9 | 22 | 12 | 6 | 2 | 20 | 34 | 0.00093 | 0.79000 | 0.05 | 0.06 | 0.0486 | 0.0557 | 0.0313 | 0.71295 | 5 | 23 | 12 | 7 | 4 | 12 | 36 | 0.00219 | 0.00058 | 0.77 | 0.15 | 0.1098 | 0.0309 | 0.0120 | 0.75302 | 1 | 24 | 12 | 8 | 6 | 14 | 28 | 0.00267 | 0.00040 | 1.42 | 0.24 | 0.2073 | 0.0137 | 0.0231 | 0.69403 | 11 | 25 | 12 | 9 | 8 | 16 | 30 | 0.00186 | 0.00037 | 3.44 | 0.19 | 0.0355 | 0.0111 | 0.0216 | 0.72975 | 3 | Mean | 8 | 7 | 6 | 16 | 32 | |
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