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.