Study on Tempering Mechanical Properties of Alloy Structural Steel Based on Multi-model Machine Learning
GAO Zhiyu1,2,*, FAN Xianjin1, GAO Sida1, XUE Weihua1
1 School of Materials Science and Engineering, Liaoning Technical University, Fuxin 123000, Liaoning, China 2 School of Materials Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
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.
通讯作者:
*高志玉,沈阳理工大学副教授、硕士研究生导师。2004年辽宁工程技术大学材料科学与工程专业本科毕业,2007年辽宁工程技术大学材料学专业硕士毕业后到辽宁工程技术大学工作,2016年1月于北京科技大学材料科学与工程专业博士毕业,2022年6月调转到沈阳理工大学工作至今。主要从事材料相变及强韧化、材料仿真计算与模拟、材料信息学与材料的智能设计等研究。在国内外学术期刊上发表论文30 余篇。包括Materials Today Communications、Journal of Adhesion Science and Technology、 Materials Research Express、Journal of Iron and Steel Research、International、《材料导报》等。zhiyugao@126.com
引用本文:
高志玉, 樊献金, 高思达, 薛维华. 基于多模型机器学习的合金结构钢回火力学性能研究[J]. 材料导报, 2023, 37(6): 21090025-7.
GAO Zhiyu, FAN Xianjin, GAO Sida, XUE Weihua. Study on Tempering Mechanical Properties of Alloy Structural Steel Based on Multi-model Machine Learning. Materials Reports, 2023, 37(6): 21090025-7.
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