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


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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    D.F. Watt, L. Coon, M. Bibby, J. Goldak, and C. Henwood, Acta Metall. 36, 3029–3035 (1988).

    Article  Google Scholar 

  2. 2.

    H. Sekiguchi and M. Inagaki, Trans. NRIM. 2, 102–125 (1960).

    Google Scholar 

  3. 3.

    Y. Liu, L.Q. Yang, B. Feng, S.W. Bai, and C.X. Xu, Mater. Sci. Forum 762, 556–561 (2013).

    Article  Google Scholar 

  4. 4.

    P.L. Harrison and R.A. Farrar, Int. Mater. Rev. 34, 35–51 (1989).

    Article  Google Scholar 

  5. 5.

    G. Krauss, Principle of heat treatment of steels, 1st ed. (Ohio: American Society for Metals, 1980), pp. 97–101.

    Google Scholar 

  6. 6.

    J. Górka, IJEMS. 22, 497–502 (2015).

    Google Scholar 

  7. 7.

    M. Xiangxu, M. Yonglin, X. Shuqing, C. Zhongyi, H. Na, and B. Qingwei, Heat Treat. Met. 40, 59–63 (2015).

    Google Scholar 

  8. 8.

    J. Sun, Z. Li, Y. Jiang, D. Li, K Zhang. Mater. Mech. Eng. 33(01), 17–19+39 (2009).

  9. 9.

    A. Pohjonen, M. Somani, and D. Porter, Comput. Mater. Sci. 150, 244–251 (2018).

    Article  Google Scholar 

  10. 10.

    J. Trzaska, A. Jagieo, and L.A. Dobrzanski, Arch. Mater. Sci. Eng. 39, 13–20 (2009).

    Google Scholar 

  11. 11.

    M. Drosback, JOM. New York 66, 334–335 (2014).

    Google Scholar 

  12. 12.

    G.J. Schmitz and U. Prahl, Integrative Computational Materials Engineering: Concepts and Applications of a Modular Simulation Platform, 1st ed. (Hoboken: Wiley, 2012).

    Book  Google Scholar 

  13. 13.

    S. Chakraborty, P.P. Chattopadhyay, S.K. Ghosh, and S. Datta, Appl. Soft Comput. 58, 297–306 (2017).

    Article  Google Scholar 

  14. 14.

    W.G. Vermulen, S. Van Der Zwaag, P. Morris, and T. Weijer, Steel Res. 68, 72–79 (1997).

    Article  Google Scholar 

  15. 15.

    J. Wang, P.J. Van Der Wolk, and S. Van Der Zwaag, ISIJ Int. 39, 1038–1046 (1999).

    Article  Google Scholar 

  16. 16.

    J. Trzaska, Arch. Mater. Sci. Eng. 82, 62–69 (2016).

    Article  Google Scholar 

  17. 17.

    S. Chakraborty, P. Das, N.K. Kaveti, P.P. Chattopadhyay, and S. Datta, Multidiscip. Model. Mater. Struct. 15, 170–186 (2019).

    Article  Google Scholar 

  18. 18.

    “MatNavi, National Institute for Materials Science Materials Database”, Accessed 23 July 2019.

  19. 19.

    S. Moeinifar, A.H. Kokabi, and H.R. Madaah Hosseini, J. Mater. Process. Technol. 211, 368–375 (2011).

    Article  Google Scholar 

  20. 20.

    J.M. Keller, M.R. Gray, and J.A. Givens, IEEE Trans. Syst. Man Cybern. 4, 580–585 (1985).

    Article  Google Scholar 

  21. 21.

    H.S. Seung, M. Opper, H. Sompolinsky, in Query by committee, Proceedings 5th Annual Workshop on Computational Learning Theory, 1st ed. (ACM Press, New York, 1992) p. 287–294

  22. 22.

    S.C. Chelgani, S.S. Matin, and S. Makaremi, Measurement 94, 416–422 (2016).

    Article  Google Scholar 

  23. 23.

    M.W. Gardner and S.R. Dorling, Atmos. Environ. 32, 2627–2636 (1998).

    Article  Google Scholar 

  24. 24.

    G.C. Cawley and N.L.C. Talbot, J. Mach. Learn. Res. 11, 2079–2107 (2010).

    MathSciNet  Google Scholar 

  25. 25.

    B. Efron, J. Am. Stat. Assoc. 78, 316–330 (1983).

    Article  Google Scholar 

  26. 26.

    R. Kohavi, IJCAI. 95, 1137–1145 (1995).

    Google Scholar 

  27. 27.

    J.J. Filliben, Technometrics 17, 111–117 (1975).

    Article  Google Scholar 

  28. 28.

    J.R. Taylor, An introduction to error analysis: the study of uncertainties in physical measurements, 2nd ed. (Sausalito: University Science Books, 1997), p. 217.

    Google Scholar 

  29. 29.

    T. Chai and R.R. Draxler, Geosci. Model Dev. 7, 1247–1250 (2014).

    Article  Google Scholar 

  30. 30.

    X. Jiang, H.Q. Yin, C. Zhang, R.J. Zhang, K.Q. Zhang, H.D. Zheng, G.Q. Liu, and X.H. Qu, Comput. Mater. Sci. 143, 295–300 (2018).

    Google Scholar 

Download references


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).

Author information



Corresponding author

Correspondence to Hao Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 443 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation