A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels

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 steel are calculated by Machine learning (ML) model. The result shows that the accuracy of the classification model is up to 100%, the predicted value of the regression model is 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.