Prediction of Continuous Cooling Transformation Diagrams for Ni-Cr-Mo Welding Steels via Machine Learning Approaches
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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.
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).
- 2.H. Sekiguchi and M. Inagaki, Trans. NRIM. 2, 102–125 (1960).Google Scholar
- 5.G. Krauss, Principle of heat treatment of steels, 1st ed. (Ohio: American Society for Metals, 1980), pp. 97–101.Google Scholar
- 6.J. Górka, IJEMS. 22, 497–502 (2015).Google Scholar
- 7.M. Xiangxu, M. Yonglin, X. Shuqing, C. Zhongyi, H. Na, and B. Qingwei, Heat Treat. Met. 40, 59–63 (2015).Google Scholar
- 8.J. Sun, Z. Li, Y. Jiang, D. Li, K Zhang. Mater. Mech. Eng. 33(01), 17–19+39 (2009).Google Scholar
- 10.J. Trzaska, A. Jagieo, and L.A. Dobrzanski, Arch. Mater. Sci. Eng. 39, 13–20 (2009).Google Scholar
- 11.M. Drosback, JOM. New York 66, 334–335 (2014).Google Scholar
- 18.“MatNavi, National Institute for Materials Science Materials Database”, https://mits.nims.go.jp/index_en.html. Accessed 23 July 2019.
- 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. 287–294Google Scholar
- 26.R. Kohavi, IJCAI. 95, 1137–1145 (1995).Google Scholar
- 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
- 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