Prediction of critical temperature A1 and A3 of medium-Mn steel based on machine learning models

doi:10.13251/j.issn.0254-6051.2025.02.044

Abstract

Abstract: In order to facilitate the design of heat treatment process of medium-Mn steel, a machine learning model for predicting the critical temperature A₁ and A₃ of medium-Mn steel was optimized. The critical temperature data of 496 groups of medium-Mn steels with different compositions were obtained by Thermal-Calc simulation software. Mn, Al and C compositions were taken as input characteristics, and phase transition temperatures A₁ and A₃ were taken as output targets. Three indexes of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R²) were used to evaluate the prediction effect of the model. From seven machine learning models (LR, DT, SVM, GPR, Boosting, Bagging and ANN), the GPR model for predicting A₁ and the GPR and ANN model for predicting A₃ were screened. The results show that the GPR model for predicting A₁ has sufficient accuracy, that is the optimal model for A₁. The grid search method is used to adjust the hyperparameters of the preliminary model for predicting A₃, and the optimal model of A₃ (single-layer ANN model) is obtained. According to the chemical composition of medium-Mn steel in the applied literature, A₁ and A₃ are predicted by using the optimal model. The overall MAE of the predicted value and measured value of A₁ and A₃ is 9.95 ℃ and 13.57 ℃, respectively, and the minimum difference is 0.30 ℃ and 6.20 ℃, respectively, indicating that the model has high accuracy and can be used to predict the critical temperature of medium-Mn steel.

Key words: machine learning, medium-Mn steel, critical temperature, parameter adjustment

CLC Number:

TG111.7

Zhang Zhiye, Wang Yan, Zhang Biao, Ji Ze, Liu Yaliang, Zhang Minghe, Feng Yunli. Prediction of critical temperature A₁ and A₃ of medium-Mn steel based on machine learning models[J]. Heat Treatment of Metals, 2025, 50(2): 268-277.

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Prediction of critical temperature A₁ and A₃ of medium-Mn steel based on machine learning models

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