Thermodynamic calculation-assisted design of 500 MPa high performance steel by machine learning

In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). T...

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Vydáno v:Journal of materials research and technology Ročník 33; s. 7061 - 7069
Hlavní autoři: Gong, Weiyi, He, Jinshan, Wang, Fan, Wang, Xitao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.11.2024
Elsevier
Témata:
Composition design
Machine learning
Microstructure stability
Multi-objective genetic optimization algorithm
Q500
Q500
Multi-objective genetic optimization algorithm
Microstructure stability
Composition design
Machine learning
ISSN:2238-7854
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Shrnutí:In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). Thermodynamic calculation has been used to obtain a large amount of data on phase fractions and mechanical properties for various chemical composition at different cooling rates. By comparing the predictive capabilities of different ML models, the deep neural network (DNN) and MOGA were combined to design Q500 wind power steel. Subsequently, three groups of designed chemical compositions were appropriately selected for verification. The results indicate that the yield strength of the experimental steel was over 500 MPa, and the maximum error in microstructure and mechanical property prediction was 5.0%. The prediction results demonstrated the rationality of the composition design framework. Meanwhile, the method of combining the material database and ML proposed in this study can also be applied to the design of other low-alloy steels.
ISSN:2238-7854
DOI:10.1016/j.jmrt.2024.10.241