Machine-Learning-Assisted Composition Design for High-Yield-Strength TWIP Steel
Abstract
:1. Introduction
2. Materials and Methods
2.1. Machine Learning Strategy for High-Yield-Strength TWIP Steel
2.2. Data Preparation and Processing
2.3. SHAP Analytical and Element Selection
2.4. Multi-Objective Optimization
2.5. Experimental Methods
3. Results
3.1. Dataset Distribution Analysis
3.2. Machine Learning Algorithm Selection
3.3. Design of High-Yield-Strength TWIP Steel
3.4. Microstructure Analysis
3.5. Analysis of Model Effectiveness
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Mass Fraction of Element/wt.% | d /μm | V /s−1 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mn | C | Al | Si | Cr | V | Nb | Ti | Cu | Mo | Ni | N | |||
Lower limit | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0.001 |
Upper limit | 30 | 1 | 3 | 3 | 4 | 0.5 | 0.3 | 0.1 | 0 | 0 | 0 | 0 | 10 | 0.001 |
Mass Fraction of Element/wt.% | d /μm | V /s−1 | YS /MPa | UTS /MPa | EL /% | SPD (GPa%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mn | C | Al | Si | Cr | V | Nb | Ti | ||||||||
#1 | Pre | 15.84 | 0.68 | 2.38 | 2.05 | 1.86 | 0.49 | 0.30 | 0.05 | 10 | 0.001 | 593 | 1050 | 52.5 | 55.1 |
Exp | 16.02 | 0.66 | 2.28 | 1.98 | 1.84 | 0.48 | 0.28 | 0.06 | 10.41 | 0.001 | 585 ± 6.3 | 1055 ± 5.7 | 55.1± | 58.1 | |
#2 | Pre | 16.88 | 0.74 | 1.71 | 1.70 | 1.15 | 0.47 | 0.01 | 0.05 | 10 | 0.001 | 553 | 1091 | 60.9 | 66.4 |
Exp | 17.05 | 0.78 | 1.65 | 1.68 | 1.20 | 0.45 | 0.01 | 0.06 | 10.32 | 0.001 | 560 ± 7.3 | 1101 ± 8.8 | 58.5 ± 2.2 | 64.4 |
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Zhou, X.; Xu, J.; Meng, L.; Wang, W.; Zhang, N.; Jiang, L. Machine-Learning-Assisted Composition Design for High-Yield-Strength TWIP Steel. Metals 2024, 14, 952. https://doi.org/10.3390/met14080952
Zhou X, Xu J, Meng L, Wang W, Zhang N, Jiang L. Machine-Learning-Assisted Composition Design for High-Yield-Strength TWIP Steel. Metals. 2024; 14(8):952. https://doi.org/10.3390/met14080952
Chicago/Turabian StyleZhou, Xiaozhou, Jiangjie Xu, Li Meng, Wenshan Wang, Ning Zhang, and Lei Jiang. 2024. "Machine-Learning-Assisted Composition Design for High-Yield-Strength TWIP Steel" Metals 14, no. 8: 952. https://doi.org/10.3390/met14080952