A Hybrid MCDM-Grey Wolf Optimizer Approach for Multi-Objective Parametric Optimization of μ-EDM Process †
Abstract
:1. Introduction
2. Methodology
2.1. PR Metamodel
2.2. Grey Wolf Optimizer
3. Results and Discussion
Parametric Optimization of μ-EDM Process
4. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Exp. no. | A (µm/s) | B (µm) | C (V) | MRR (mm3/min) | Ra (µm) | TWR |
---|---|---|---|---|---|---|
1 | 2 | 0.1 | 80 | 0.003 | 0.36 | 1.64 |
2 | 4 | 0.1 | 80 | 0.008 | 0.38 | 0.54 |
3 | 6 | 0.1 | 80 | 0.01 | 0.38 | 1.78 |
4 | 2 | 0.1 | 100 | 0.004 | 0.41 | 2.38 |
5 | 4 | 0.1 | 100 | 0.008 | 0.45 | 1.32 |
6 | 6 | 0.1 | 100 | 0.008 | 0.52 | 2.32 |
7 | 2 | 0.1 | 120 | 0.007 | 0.42 | 2.68 |
8 | 4 | 0.1 | 120 | 0.017 | 0.45 | 0.89 |
9 | 6 | 0.1 | 120 | 0.012 | 0.54 | 1.92 |
10 | 2 | 1 | 80 | 0.004 | 1.1 | 2.23 |
11 | 4 | 1 | 80 | 0.01 | 1.2 | 1.32 |
12 | 6 | 1 | 80 | 0.01 | 1.2 | 2.35 |
13 | 2 | 1 | 100 | 0.007 | 1.9 | 2.68 |
14 | 4 | 1 | 100 | 0.018 | 1.82 | 1.28 |
15 | 6 | 1 | 100 | 0.016 | 1.88 | 2.62 |
16 | 2 | 1 | 120 | 0.008 | 1.9 | 2.88 |
17 | 4 | 1 | 120 | 0.038 | 1.82 | 1.19 |
18 | 6 | 1 | 120 | 0.018 | 1.82 | 2.23 |
19 | 2 | 10 | 80 | 0.007 | 3.8 | 2.46 |
20 | 4 | 10 | 80 | 0.011 | 3.7 | 1.23 |
21 | 6 | 10 | 80 | 0.013 | 3.7 | 2.78 |
22 | 2 | 10 | 100 | 0.01 | 4.2 | 2.92 |
23 | 4 | 10 | 100 | 0.036 | 4.8 | 1.82 |
24 | 6 | 10 | 100 | 0.012 | 4.4 | 2.82 |
25 | 2 | 10 | 120 | 0.022 | 5.2 | 3.16 |
26 | 4 | 10 | 120 | 0.046 | 5.9 | 1.58 |
27 | 6 | 10 | 120 | 0.014 | 4.8 | 3.12 |
28 | 4 | 1 | 100 | 0.009 | 1.88 | 1.2 |
29 | 4 | 1 | 100 | 0.052 | 1.86 | 1.48 |
30 | 4 | 1 | 100 | 0.024 | 1.65 | 1.38 |
31 | 4 | 1 | 100 | 0.014 | 1.74 | 1.36 |
32 | 4 | 1 | 100 | 0.022 | 1.7 | 1.54 |
Condition | Output | A (µm/s) | B (µm) | C (V) | Optimal Value |
---|---|---|---|---|---|
Single-objective | MRR (mm3/min) | 3.9045 | 5.9608 | 120 | 0.047143 |
Ra (µm) | 2 | 0.1 | 80 | 0.16287 | |
TWR | 3.9195 | 0.1 | 80 | 0.63627 | |
Multi-objective | MRR (mm3/min) | 3.55 | 0.158 | 80 | 0.0126 |
Ra (µm) | 0.3892 | ||||
TWR | 0.7042 |
Exp No. | TOPSIS Score | MABAC Score | COPRAS Score |
---|---|---|---|
1 | 0.4161 | −0.5082 | 0.0313 |
2 | 0.4913 | −0.3125 | 0.0751 |
3 | 0.449 | −0.4809 | 0.034 |
4 | 0.3887 | −0.6125 | 0.0235 |
5 | 0.455 | −0.4306 | 0.0395 |
6 | 0.407 | −0.5811 | 0.0261 |
7 | 0.392 | −0.6361 | 0.0234 |
8 | 0.5439 | −0.3045 | 0.058 |
9 | 0.4505 | −0.4953 | 0.0324 |
10 | 0.365 | −0.6243 | 0.0214 |
11 | 0.4421 | −0.4535 | 0.0329 |
12 | 0.3881 | −0.605 | 0.0244 |
13 | 0.3204 | −0.7089 | 0.0188 |
1 4 | 0.4884 | −0.4222 | 0.0346 |
15 | 0.3882 | −0.6361 | 0.0253 |
16 | 0.3185 | −0.7313 | 0.0188 |
17 | 0.7263 | −0.2691 | 0.0492 |
18 | 0.4304 | −0.5619 | 0.0284 |
19 | 0.2174 | −0.7699 | 0.0159 |
20 | 0.3546 | −0.5561 | 0.0226 |
21 | 0.2523 | −0.7701 | 0.0195 |
22 | 0.187 | −0.8362 | 0.0166 |
23 | 0.5235 | −0.5222 | 0.0358 |
24 | 0.1997 | −0.8173 | 0.0179 |
25 | 0.2822 | −0.8367 | 0.0235 |
26 | 0.5721 | −0.471 | 0.0417 |
27 | 0.1927 | −0.8671 | 0.0184 |
28 | 0.416 | −0.4762 | 0.0288 |
29 | 0.8128 | −0.2159 | 0.0561 |
30 | 0.5524 | −0.3866 | 0.0388 |
31 | 0.4501 | −0.4579 | 0.0316 |
32 | 0.5187 | −0.4265 | 0.0358 |
Method | A (µm/s) | B (µm) | C (V) | MRR (mm3/min) | Ra (µm) | TWR | Comp. Time (s) |
---|---|---|---|---|---|---|---|
PR-WSMO-GWO | 3.55 | 0.158 | 80 | 0.0126 | 0.3892 | 0.7042 | 1.03886 |
TOPSIS-PR-GWO | 3.9578 | 0.1 | 80 | 0.0134 | 0.3245 | 0.6367 | 1.02534 |
Improvement % | - | - | - | 6.35 | 16.62 | 9.58 | 1.3 |
PR-WSMO-GWO | 3.55 | 0.158 | 80 | 0.0126 | 0.3892 | 0.7042 | 1.03886 |
MABAC-PR-GWO | 3.8578 | 0.1 | 80 | 0.01232 | 0.3224 | 0.6375 | 1.00352 |
Improvement % | - | - | - | -2.22 | 17.16 | 9.47 | 3.4 |
PR-WSMO-GWO | 3.55 | 0.158 | 80 | 0.0126 | 0.3892 | 0.7042 | 1.03886 |
COPRAS-PR-GWO | 3.8578 | 0.1 | 80 | 0.01232 | 0.3224 | 0.6375 | 1.01936 |
Improvement % | - | - | - | -2.22 | 17.16 | 9.47 | 1.88 |
Method | Parametric Combination | Normalized Response Values | Sum | ||||
---|---|---|---|---|---|---|---|
A | B | C | MRR | Ra | TWR | ||
PR-WSMO-GWO | 3.55 | 0.158 | 80 | 0.1959 | 0.988 | 0.9373 | 2.1212 |
TOPSIS-PR-GWO | 3.9578 | 0.1 | 80 | 0.2122 | 0.9996 | 0.9631 | 2.1749 |
MABAC-PR-GWO | 3.8578 | 0.1 | 80 | 0.1902 | 1 | 0.9628 | 2.153 |
COPRAS-PR-GWO | 3.8578 | 0.1 | 80 | 0.1902 | 1 | 0.9628 | 2.153 |
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Das, P.P. A Hybrid MCDM-Grey Wolf Optimizer Approach for Multi-Objective Parametric Optimization of μ-EDM Process. Eng. Proc. 2023, 59, 112. https://doi.org/10.3390/engproc2023059112
Das PP. A Hybrid MCDM-Grey Wolf Optimizer Approach for Multi-Objective Parametric Optimization of μ-EDM Process. Engineering Proceedings. 2023; 59(1):112. https://doi.org/10.3390/engproc2023059112
Chicago/Turabian StyleDas, Partha Protim. 2023. "A Hybrid MCDM-Grey Wolf Optimizer Approach for Multi-Objective Parametric Optimization of μ-EDM Process" Engineering Proceedings 59, no. 1: 112. https://doi.org/10.3390/engproc2023059112