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Prediction of Continuous Cooling Transformation Diagrams for Ni-Cr-Mo Welding Steels via Machine Learning Approaches

  • Machine Learning Applications in Advanced Manufacturing Processes
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Abstract

Continuous cooling transformation diagrams in synthetic weld heat-affected zones (SH-CCT diagrams) are important tools to analyze the microstructure and mechanical properties of the heat-affected zone under certain welding conditions and to evaluate the weldability of steel. In this study, various machine-learning approaches are used to select an appropriate model for prediction of SH-CCT diagrams for Ni-Cr-Mo steels using relevant material descriptors including the chemical compositions and cooling rate. Random forest is the best model to predict the ferrite and bainite transition start temperature accurately, K-nearest neighbors is suitable for predicting the start temperature of martensite transformation, and random committee is used to predict the hardness. These optimal models are used to predict the SH-CCT diagrams of five kinds of steels to verify the accuracy. The results show that the predicted values of the optimal models agree well with the experimental data with a strong correlation coefficient and low error value.

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Acknowledgements

The authors acknowledge the financial support from the National Key Research and Development Program of China (No. 2017YFB0903901), the National Natural Science Foundation of China (No. 51571020), the Fundamental Research Funds for the Central Universities (Project No. FRF-IC-19-003), the State Key Laboratory for Advanced Metals and Materials (No. 2019Z-6) and the Fundamental Research Funds for the Liaoning Universities (LJ2017QL006).

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Authors and Affiliations

  1. School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, China

    Xiaoxiao Geng & Hao Wang

  2. Department of Mathematical Sciences, Karakoram International University Gilgit, Gilgit-Baltistan, 15100, Pakistan

    Asad Ullah

  3. School of Materials Science and Engineering, Liaoning Technical University, Fuxin, China

    Weihua Xue

  4. College of Materials and Metallurgy, Guizhou University, Guiyang, China

    Song Xiang

  5. Metallurgical Technology Institute, Central Iron and Steel Research Institute, Beijing, China

    Li Meng

  6. State Key Laboratory of Advanced Power Transmission Technology, Global Energy Interconnection Research Institute Co., Ltd, Beijing, China

    Guang Ma

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  1. Xiaoxiao Geng
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  2. Hao Wang
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  4. Weihua Xue
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Correspondence to Hao Wang.

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Geng, X., Wang, H., Ullah, A. et al. Prediction of Continuous Cooling Transformation Diagrams for Ni-Cr-Mo Welding Steels via Machine Learning Approaches. JOM 72, 3926–3934 (2020). https://doi.org/10.1007/s11837-020-04057-z

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  • DOI: https://doi.org/10.1007/s11837-020-04057-z

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