Multi-Objective Optimization of Functionally Graded Beams Using a Genetic Algorithm with Non-Dominated Sorting
Abstract
:1. Introduction
2. Effective Material Properties
2.1. The Rule of Mixtures
2.2. The Mori-Tanaka Scheme
3. The Mixed LW HSDT for FG Beams
3.1. Strong-Form Formulation
3.2. Applications
4. Optimal Design
4.1. Statement of the Optimization Problem
4.2. The Non-Dominated Sorting-Based GA
- (a)
- Initial population
- (b)
- Fitness value
- (c)
- Selection
- (d)
- Crossover
- (e)
- Mutation
- (f)
- Elitist
- (g)
- End criteria
5. Illustrative Examples
5.1. Thermal Buckling Analysis of Laminated Composite Beams and FG Beams
5.2. Optimization of Material Composition of FG beams
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Relations between the Generalized Force/Moment Resultants and the Generalized Displacements
References
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L/h | Theories | |
---|---|---|
10 | Current LW1 (nl = 1) | 0.8315 |
Current LW1 (nl = 3) | 0.8123 | |
Current LW1 (nl = 6) | 0.7995 | |
Current LW1 (nl = 9) | 0.7970 | |
Current LW2 (nl = 1) | 0.8306 | |
Current LW2 (nl = 3) | 0.7953 | |
Current LW2 (nl = 6) | 0.7950 | |
Current LW3 (nl = 1) | 0.8010 | |
Current LW3 (nl = 3) | 0.7950 | |
Current LW3 (nl = 6) | 0.7950 | |
a Current LW3 (nl = 6) | 0.8001 | |
b Current LW3 (nl = 6) | 0.8383 | |
a, b Current LW3 (nl = 6) | 0.8438 | |
HSDT [80] | 0.78912 | |
HRSDT [22] | 0.8230 | |
TSDT [79] | 0.8229 | |
FSDT [79] | 0.8281 | |
EBT [79] | 1.1072 | |
50 | Current LW1 (nl = 1) | 1.0411 |
Current LW1 (nl = 3) | 1.0370 | |
Current LW1 (nl = 6) | 1.0359 | |
Current LW1 (nl = 9) | 1.0357 | |
Current LW2 (nl = 1) | 1.0392 | |
Current LW2 (nl = 3) | 1.0355 | |
Current LW2 (nl = 6) | 1.0355 | |
Current LW3 (nl = 1) | 1.0373 | |
Current LW3 (nl = 3) | 1.0355 | |
Current LW3 (nl = 6) | 1.0355 | |
a Current LW3 (nl = 6) | 1.0360 | |
b Current LW3 (nl = 6) | 1.0931 | |
a, b Current LW3 (nl = 6) | 1.0936 | |
HSDT [80] | 1.04656 | |
HRSDT [22] | 1.0921 | |
TSDT [79] | 1.0921 | |
FSDT [79] | 1.0925 | |
EBT [79] | 1.1072 |
Theories | ||
---|---|---|
10 | Current LW1 (nl = 1) | 0.8335 |
Current LW1 (nl = 3) | 0.8162 | |
Current LW1 (nl = 6) | 0.8088 | |
Current LW1 (nl = 9) | 0.8073 | |
Current LW2 (nl = 1) | 0.8302 | |
Current LW2 (nl = 3) | 0.8063 | |
Current LW2 (nl = 6) | 0.8062 | |
Current LW3 (nl = 1) | 0.8134 | |
Current LW3 (nl = 3) | 0.8061 | |
Current LW3 (nl = 6) | 0.8061 | |
a Current LW3 (nl = 6) | 0.8123 | |
b Current LW3 (nl = 6) | 0.8896 | |
a, b Current LW3 (nl = 6) | 0.8965 | |
HSDT [80] | 0.81683 | |
HRSDT [22] | 0.8833 | |
TSDT [79] | 0.8832 | |
FSDT [79] | 0.8868 | |
EBT [79] | 1.0370 | |
30 | Current LW1 (nl = 1) | 0.7828 |
Current LW1 (nl = 3) | 0.7622 | |
Current LW1 (nl = 6) | 0.7463 | |
Current LW1 (nl = 9) | 0.7432 | |
Current LW2 (nl = 1) | 0.7824 | |
Current LW2 (nl = 3) | 0.7411 | |
Current LW2 (nl = 6) | 0.7407 | |
Current LW3 (nl = 1) | 0.7454 | |
Current LW3 (nl = 3) | 0.7406 | |
Current LW3 (nl = 6) | 0.7406 | |
a Current LW3 (nl = 6) | 0.7445 | |
b Current LW3 (nl = 6) | 0.7680 | |
a, b Current LW3 (nl = 6) | 0.7720 | |
HSDT [80] | 0.72608 | |
HRSDT [22] | 0.7472 | |
TSDT [79] | 0.7471 | |
FSDT [79] | 0.7528 | |
EBT [79] | 1.1329 |
L/h | Micromechanical Models | Theories | |||
---|---|---|---|---|---|
10 | 1 | Mori–Tanaka | Current LW1 (nl = 2) | 0.5151 | 0.3757 |
Current LW1 (nl = 4) | 0.5043 | 0.3692 | |||
Current LW1 (nl = 8) | 0.5015 | 0.3674 | |||
Current LW2 (nl = 2) | 0.5007 | 0.3674 | |||
Current LW2 (nl = 4) | 0.5006 | 0.3667 | |||
Current LW3 (nl = 2) | 0.5006 | 0.3667 | |||
Current LW3 (nl = 4) | 0.5006 | 0.3667 | |||
10 | 1 | Rule of mixtures | Current LW1 (nl = 2) | 0.5122 | 0.3678 |
Current LW1 (nl = 4) | 0.5014 | 0.3614 | |||
Current LW1 (nl = 8) | 0.4987 | 0.3600 | |||
Current LW2 (nl = 2) | 0.4978 | 0.3595 | |||
Current LW2 (nl = 4) | 0.4978 | 0.3595 | |||
Current LW3 (nl = 2) | 0.4977 | 0.3595 | |||
Current LW3 (nl = 4) | 0.4977 | 0.3595 | |||
10 | 5 | Mori–Tanaka | Current LW1 (nl = 2) | 0.5315 | 0.4525 |
Current LW1 (nl = 4) | 0.5202 | 0.4436 | |||
Current LW1 (nl = 8) | 0.5172 | 0.4412 | |||
Current LW2 (nl = 2) | 0.5164 | 0.4405 | |||
Current LW2 (nl = 4) | 0.5163 | 0.4405 | |||
Current LW3 (nl = 2) | 0.5163 | 0.4405 | |||
Current LW3 (nl = 4) | 0.5163 | 0.4405 | |||
10 | 5 | Rule of mixtures | Current LW1 (nl = 2) | 0.5307 | 0.4467 |
Current LW1 (nl = 4) | 0.5193 | 0.4381 | |||
Current LW1 (nl = 8) | 0.5164 | 0.4358 | |||
Current LW2 (nl = 2) | 0.5155 | 0.4350 | |||
Current LW2 (nl = 4) | 0.5154 | 0.4350 | |||
Current LW3 (nl = 2) | 0.5154 | 0.4350 | |||
Current LW3 (nl = 4) | 0.5154 | 0.4350 | |||
20 | 5 | Mori–Tanaka | Current LW1 (nl = 2) | 0.5432 | 0.5194 |
Current LW1 (nl = 4) | 0.5322 | 0.5081 | |||
Current LW1 (nl = 8) | 0.5295 | 0.5066 | |||
Current LW2 (nl = 2) | 0.5286 | 0.5042 | |||
Current LW2 (nl = 4) | 0.5285 | 0.5042 | |||
Current LW3 (nl = 2) | 0.5285 | 0.5042 | |||
Current LW3 (nl = 4) | 0.5285 | 0.5042 | |||
20 | 5 | Rule of mixtures | Current LW1 (nl = 2) | 0.5423 | 0.5170 |
Current LW1 (nl = 4) | 0.5314 | 0.5066 | |||
Current LW1 (nl = 8) | 0.5286 | 0.5042 | |||
Current LW2 (nl = 2) | 0.5277 | 0.5026 | |||
Current LW2 (nl = 4) | 0.5277 | 0.5026 | |||
Current LW3 (nl = 2) | 0.5277 | 0.5026 | |||
Current LW3 (nl = 4) | 0.5277 | 0.5026 |
Materials | P0 | P−1 | P1 | P2 | P3 | P at 300K |
---|---|---|---|---|---|---|
ZrO2 | ||||||
E | 244.27 × 109 | 0 | −1.371 × 10−3 | 1.214 × 10−6 | −3.681 × 10−10 | 168.06 × 109 |
12.766 × 10−6 | 0 | −1.491 × 10−3 | 1.006 × 10−5 | −6.778 × 10−11 | 18.591 × 10−6 | |
0.2882 | 0 | 1.133 × 10−4 | 0 | 0 | 0.298 | |
3657 | 0 | 0 | 0 | 0 | 3657 | |
SUS304 | ||||||
E | 201.04 × 109 | 0 | 3.079 ×10−4 | −6.534 × 10−7 | 0 | 207.79 × 109 |
12.330 × 10−6 | 0 | 8.086 × 10−4 | 0 | 0 | 15.321 × 10−6 | |
0.3262 | 0 | −2.002 × 10−4 | 3.797 × 10−7 | 0 | 0.318 | |
8166 | 0 | 0 | 0 | 0 | 8166 |
Order Number of Pareto- Optimal Solutions | (F1, F2) | ||||
---|---|---|---|---|---|
1 | (0.000, 1.000) | 3658.1 | 300.7326 | (0.358, 1.829, 0.000) | (0.565, 0.435) |
10 | (0.037, 0.953) | 3820.7 | 303.1011 | (0.000, 1.000, 0.037) | (0.558, 0.442) |
20 | (0.072, 0.910) | 3975.6 | 305.2840 | (0.000, 2.392, 0.076) | (0.549, 0.451) |
30 | (0.113, 0.861) | 4157.9 | 307.7473 | (0.000, 3.000, 0.125) | (0.537, 0.463) |
40 | (0.154, 0.815) | 4337.3 | 310.0472 | (0.000, 2.831, 0.178) | (0.523, 0.477) |
50 | (0.206, 0.760) | 4565.6 | 312.7782 | (0.000, 2.759, 0.253) | (0.502, 0.498) |
60 | (0.251, 0.716) | 4768.5 | 315.0162 | (0.000, 2.714, 0.328) | (0.482, 0.518) |
70 | (0.309, 0.665) | 5023.2 | 317.5743 | (0.000, 3.000, 0.435) | (0.456, 0.544) |
80 | (0.380, 0.610) | 5335.0 | 320.3524 | (0.000, 2.967, 0.591) | (0.424, 0.576) |
90 | (0.433, 0.572) | 5570.0 | 322.2288 | (0.002, 2.958, 0.736) | (0.403, 0.597) |
100 | (0.496, 0.531) | 5851.2 | 324.2938 | (0.001, 2.958, 0.945) | (0.385, 0.615) |
110 | (0.576, 0.482) | 6204.6 | 326.7625 | (0.004, 2.820, 1.300) | (0.381, 0.619) |
120 | (0.644, 0.439) | 6502.7 | 328.9122 | (0.000, 3.000, 1.723) | (0.399, 0.601) |
130 | (0.695, 0.403) | 6731.2 | 330.7254 | (0.000, 2.745, 2.190) | (0.428, 0.572) |
140 | (0.741, 0.367) | 6934.5 | 332.5478 | (0.653, 1.122, 7.805) | (0.459, 0.541) |
150 | (0.801, 0.311) | 7199.1 | 335.3457 | (0.864, 1.065, 22.95) | (0.506, 0.494) |
160 | (0.844, 0.264) | 7387.2 | 337.7239 | (0.864, 1.082, 25.69) | (0.545, 0.455) |
170 | (0.883, 0.213) | 7559.9 | 340.2622 | (0.881, 1.179, 24.36) | (0.580, 0.420) |
180 | (0.926, 0.148) | 7751.7 | 343.5581 | (0.954, 1.146, 50.00) | (0.621, 0.379) |
190 | (0.967, 0.076) | 7930.0 | 347.1474 | (0.537, 1.000, 45.04) | (0.661, 0.339) |
200 | (1.000, 0.008) | 8077.5 | 350.5544 | (0.000, 2.005, 50.00) | (0.684, 0.316) |
Order Number of Pareto- Optimal Solutions | (F1, F2) | ||||
---|---|---|---|---|---|
1 | (0.000, 0.994) | 3657.0 | 172.5 | (0.465, 1.620, 0.000) | (0.272, 0.728) |
10 | (0.040, 0.977) | 3833.8 | 174.4 | (0.000, 3.000, 0.041) | (0.315, 0.685) |
20 | (0.098, 0.947) | 4090.2 | 177.7 | (0.000, 3.000, 0.105) | (0.354, 0.646) |
30 | (0.151, 0.917) | 4326.6 | 181.1 | (0.000, 1.799, 0.173) | (0.375, 0.625) |
40 | (0.199, 0.888) | 4535.2 | 184.4 | (0.000, 1.787, 0.241) | (0.385, 0.615) |
50 | (0.246, 0.858) | 4743.0 | 187.7 | (0.000, 3.000, 0.318) | (0.390, 0.610) |
60 | (0.311, 0.816) | 5031.9 | 192.4 | (0.000, 1.000, 0.440) | (0.391, 0.609) |
70 | (0.364, 0.781) | 5268.3 | 196.2 | (0.000, 2.613, 0.560) | (0.391, 0.609) |
80 | (0.424, 0.743) | 5532.8 | 200.6 | (0.000, 1.561, 0.716) | (0.393, 0.607) |
90 | (0.483, 0.705) | 5791.1 | 204.8 | (0.000, 1.474, 0.901) | (0.400, 0.600) |
100 | (0.531, 0.672) | 6002.2 | 208.5 | (0.000, 2.792, 1.086) | (0.411, 0.589) |
110 | (0.585, 0.633) | 6242.9 | 212.9 | (0.013, 1.444, 1.364) | (0.431, 0.569) |
120 | (0.651, 0.579) | 6533.8 | 218.8 | (0.000, 2.239, 1.746) | (0.465, 0.535) |
130 | (0.697, 0.537) | 6736.6 | 223.6 | (0.000, 1.443, 2.132) | (0.496, 0.504) |
140 | (0.746, 0.484) | 6956.2 | 229.5 | (0.046, 2.894, 2.754) | (0.535, 0.465) |
150 | (0.793, 0.425) | 7164.5 | 236.1 | (0.343, 1.047, 5.655) | (0.575, 0.425) |
160 | (0.836, 0.362) | 7353.8 | 243.1 | (0.373, 1.592, 5.894) | (0.612, 0.388) |
170 | (0.884, 0.281) | 7562.9 | 252.1 | (0.428, 1.210, 10.03) | (0.652, 0.348) |
180 | (0.915, 0.219) | 7703.5 | 259.1 | (0.798, 1.231, 21.23) | (0.677, 0.323) |
190 | (0.965, 0.105) | 7924.0 | 271.9 | (0.879, 1.297, 34.51) | (0.713, 0.287) |
200 | (1.000, 0.013) | 8077.6 | 282.1 | (0.117, 1.000, 49.74) | (0.736, 0.264) |
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Wu, C.-P.; Li, K.-W. Multi-Objective Optimization of Functionally Graded Beams Using a Genetic Algorithm with Non-Dominated Sorting. J. Compos. Sci. 2021, 5, 92. https://doi.org/10.3390/jcs5040092
Wu C-P, Li K-W. Multi-Objective Optimization of Functionally Graded Beams Using a Genetic Algorithm with Non-Dominated Sorting. Journal of Composites Science. 2021; 5(4):92. https://doi.org/10.3390/jcs5040092
Chicago/Turabian StyleWu, Chih-Ping, and Kuan-Wei Li. 2021. "Multi-Objective Optimization of Functionally Graded Beams Using a Genetic Algorithm with Non-Dominated Sorting" Journal of Composites Science 5, no. 4: 92. https://doi.org/10.3390/jcs5040092
APA StyleWu, C. -P., & Li, K. -W. (2021). Multi-Objective Optimization of Functionally Graded Beams Using a Genetic Algorithm with Non-Dominated Sorting. Journal of Composites Science, 5(4), 92. https://doi.org/10.3390/jcs5040092