基于多模型机器学习的合金结构钢回火力学性能研究

doi:10.11896/cldb.21090025

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Abstract The tempering mechanical property curve of steel is the basis for the optimization of tempering process parameters, and machine learning provides a new and efficient way for the optimization of tempering process parameters. In this paper, many kinds of machine learning model algorithms such as ANN, IBK, Bagging, Randomtree and M5Rules are used to predict the tempering mechanical properties of alloy structural steel. The tempering performance data of 2950 groups of steels are extracted from the National Materials Science Data Sharing Network, with chemical composition and tempering temperature as input characteristics, and tensile strength, yield strength and Vickers hardness as output targets. Correlation coefficient (R), root mean square error (RMSE), average absolute error (MAE) and relative error (δ) are used to evaluate and finalize the model. The results show that higher prediction accuracy can be obtained by using IBK, Randomtree and Bagging algorithms to predict tempering tensile strength, yield strength and Vickers hardness, and the relative errors are concentrated between ±6%,±10% and ±10%, respectively. The best model is used to predict the tempering mechanical properties of four kinds of steel in the test set, and the prediction accuracy is higher than that of JMatPro software and empirical formula. Limited to the unbalanced distribution of the existing composition characteristic data, in order to further expand the prediction accuracy and generalization of the model, it is necessary to accumulate more data about microalloy V, Ti, B and so on.

Key words： machine learning tempering mechanical property alloy structural steel algorithmic model

Published: Online: 2023-03-27

CLC number:	TG161
	TG156

Fund:Basic Scientific Research Project of Education Department of Liaoning Province for Colleges and Universities (LJ2020JCL021, LJKMZ20220593).

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	GAO Zhiyu
	FAN Xianjin
	GAO Sida
	XUE Weihua

Cite this article:

GAO Zhiyu,FAN Xianjin,GAO Sida, et al. Study on Tempering Mechanical Properties of Alloy Structural Steel Based on Multi-model Machine Learning[J]. Materials Reports, 2023, 37(6): 21090025-7.

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http://www.mater-rep.com/EN/10.11896/cldb.21090025 OR http://www.mater-rep.com/EN/Y2023/V37/I6/21090025

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