A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels. (30th April 2022)

• Record Type: Journal Article
• Authors: Geng, Xiaoxiao; Mao, Xinping; Wu, Hong-Hui; Wang, Shuize; Xue, Weihua; Zhang, Guanzhen; Ullah, Asad; Wang, Hao
• Is Part Of: Journal of materials science & technology. Volume 107(2022)
• Page Start: 207
• Page End: 215
• Publication Date: 2022-04-30
• Subjects: Continuous cooling transformation -- Heat-affected zone -- Machine learning -- Symbolic regression; Metals -- Periodicals; Materials science -- Periodicals; Materials science; Metals; Periodicals; 620.1105
• DOI: 10.1016/j.jmst.2021.07.038
• Languages: English
• ISSNs: 1005-0302

Abstract

Highlights: The hybrid machine learning model is established to predict SH-CCT diagrams of steels. The predicted values of models have a high consistency with the experimental values. The mathematical expression of hardness is given accurately by symbolic regression. It can guide the welding process with a desired microstructure and properties. Abstract: Continuous cooling transformation diagrams in synthetic weld heat-affected zone (SH-CCT diagrams) show the phase transition temperature and hardness at different cooling rates, which is an important basis for formulating the welding process or predicting the performance of welding heat-affected zone. However, the experimental determination of SH-CCT diagrams is a time-consuming and costly process, which does not conform to the development trend of new materials. In addition, the prediction of SH-CCT diagrams using metallurgical models remains a challenge due to the complexity of alloying elements and welding processes. So, in this study, a hybrid machine learning model consisting of multilayer perceptron classifier, k-Nearest Neighbors and random forest is established to predict the phase transformation temperature and hardness of low alloy steel using chemical composition and cooling rate. Then the SH-CCT diagrams of 6 kinds of steels are calculated by the hybrid machine learning model. The results show that the accuracy of the classification model is up to 100%, the predicted values of the regression models are in good agreement with the experimental results, with high correlation coefficient and low error value. Moreover, the mathematical expressions of hardness in welding heat-affected zone of low alloy steel are calculated by symbolic regression, which can quantitatively express the relationship between alloy composition, cooling time and hardness. This study demonstrates the great potential of the material informatics in the field of welding technology.