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Modeling Continuous Cooling Transformations for HSLA Steels With Physical Metallurgy Guided Hereditary Machine Learning

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Abstract

Phase transformations during continuous cooling play a vital role in controlling final microstructure and mechanical properties of hot-rolled high-strength low-alloy (HSLA) steels. Therefore, accurate prediction of continuous cooling transformation (CCT) diagrams is the key to optimizing hot-rolling processes. But, because phase transformation behaviors are complex and the accumulated data are insufficient, it is of great difficulty to accurately model CCT diagrams. In this paper, a hereditary modeling method based on the combination theories of physical metallurgy (TPM) and machine learning (ML) is proposed. Through thermodynamic and kinetic analyses, the key factors affecting behaviors of continuous cooling transformation are clarified. Combined with the existed data, the feature parameters in direct correlations with phase transformation temperatures are obtained by theoretical calculations. By using the algorithm of support vector machine (SVM), the model for predicting CCT diagrams has been developed, demonstrating superior prediction accuracy over the traditional data-driven ML models, especially in predicting the temperatures for pearlite and bainite transformations. By applying the established ML models to industrial production of HSLA steel plates, their CCT diagrams were predicted and verified through metallographic observations of final microstructures formed under different cooling paths.

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Acknowledgments

The authors should like to acknowledge the financial support from the Ministry of Science and Technology, China (Grant No. 2022YFB3304800), the Postdoctoral Science Foundation of China (Grant No. 2022T150205), the National Natural Science Foundation of China (Grant No. 52104370), and the Postdoctoral Research Fund for Northeastern University (Grant No. 20210203).

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The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. State Key Laboratory of Rolling and Automation, Northeastern University, P O Box 105, Shenyang, 110819, Liaoning, P.R. China

    Yang Cao, Guangming Cao, Chunyuan Cui, Xin Li, Siwei Wu & Zhenyu Liu

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  1. Yang Cao
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Correspondence to Guangming Cao or Zhenyu Liu.

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Cao, Y., Cao, G., Cui, C. et al. Modeling Continuous Cooling Transformations for HSLA Steels With Physical Metallurgy Guided Hereditary Machine Learning. Metall Mater Trans A 54, 4891–4904 (2023). https://doi.org/10.1007/s11661-023-07210-w

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