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 per-ceptron classifier,k-Nearest Neighbors and random forest is established to predict the phase transforma-tion 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 be-tween alloy composition,cooling time and hardness.This study demonstrates the great potential of the material informatics in the field of welding technology.

Xiaoxiao Geng;Xinping Mao;Hong-Hui Wu;Shuize Wang;Weihua Xue;Guanzhen Zhang;Asad Ullah;Hao Wang

Beijing Advanced Innovation Center for Materials Genome Engineering,Collaborative Innovation Center of Steel Technology,University of Science and Technology Beijing,Beijing 100083,China;School of Materials Science and Engineering,University of Science and Technology Beijing,Beijing 100083,China;School of Materials Science and Engineering,Liaoning Technical University,Fuxin 123000,China;Metals and Chemistry Research Institute,China Academy of Railway Sciences,Beijing 100081,China;Department of Mathematical Sciences,Karakoram International University,Gilgit-Baltistan,15100,Pakistan

材料科学技术（英文版）

[1]Xiaoxiao Geng;Xinping Mao;Hong-Hui Wu;Shuize Wang;Weihua Xue;Guanzhen Zhang;Asad Ullah;Hao Wang-.A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels)[J].材料科学技术（英文版）,2022(12):207-215

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