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

材料导报

2023, Vol. 37

Issue (6): 21090025-7 https://doi.org/10.11896/cldb.21090025

金属与金属基复合材料

基于多模型机器学习的合金结构钢回火力学性能研究

高志玉^1,2,*, 樊献金¹, 高思达¹, 薛维华¹

1 辽宁工程技术大学材料科学与工程学院,辽宁阜新 123000
2 沈阳理工大学材料科学与工程学院,沈阳 110159

Study on Tempering Mechanical Properties of Alloy Structural Steel Based on Multi-model Machine Learning

GAO Zhiyu^1,2,*, 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

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摘要钢的回火力学性能曲线是回火工艺参数优选的基础,机器学习为回火工艺参数的优选提供了新的高效途径。本工作采用ANN、IBK、Bagging、Randomtree和M5Rules等多种机器学习模型算法预测合金结构钢的回火力学性能。2950组钢回火性能数据提取于国家材料科学数据共享网,以化学成分、回火温度作为输入特征,以抗拉强度、屈服强度和维氏硬度作为输出目标。采用相关系数(R)、均方根误差(RMSE)、平均绝对误差(MAE)和相对误差(δ)进行模型的评估与定型。结果表明:回火抗拉强度、屈服强度、维氏硬度的预测分别采用IBK、Randomtree和Bagging算法能够得到更高的预测精度,相对误差分别集中于±6%、±10%、±10%。使用最佳模型对测试集四种钢的回火力学性能预测结果良好,预测精度高于JMatPro软件和经验公式的计算结果。限于已有成分特征数据分布的不均衡,为进一步扩展模型的预测精度与泛化性,需积累更多关于微合金V、Ti、B等方面的数据。

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关键词: 机器学习回火力学性能合金结构钢算法模型

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

发布日期: 2023-03-27

ZTFLH:	TG161
	TG156

基金资助: 辽宁省教育厅高等学校基本科研项目(LJ2020JCL021;LJKMZ20220593)

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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http://www.mater-rep.com/CN/10.11896/cldb.21090025 或 http://www.mater-rep.com/CN/Y2023/V37/I6/21090025

1 Ding F X, Lan L F, Yu Y J, et al. Construction and Building Materials, 2020, 241, 118020.
2 Wen J X, Yu L, Cao R, et al. Materials Reports, 2021, 35(S1), 381 (in Chinese).
温俊霞, 余磊, 曹睿, 等. 材料导报, 2021, 35(S1), 381.
3 Ghosh S K, Chattopadhyay P P, Haldar A, et al. The Iron and Steel Institute of Japan, 2008, 48(5), 649.
4 Mandal A, Bandyopadhyay T K. Steel Research International, 2017, 88(6), 1600317.
5 Kim D H, Lee Y, Kim B M. Journal of Materials Processing Technology, 2002, 130, 214.
6 Kang J H, Park I W, Jae J S, et al. Journal of Materials Processing Technology, 1999, 96(1), 53.
7 Gariboldi E, Ripamonti D, Vedani M. Defect and Diffusion Forum, 2010, 297, 608.
8 Timoshenkov A, Warczok P, Albu M, et al. Computational Materials Science, 2014, 94, 85.
9 Chakraborty S, Das P, Kaveti N K, et al. Multidiscipline Modeling in Materials and Structures, 2019, 15(1), 1573.
10 Wu S W, Zhou X G, Cao G M, et al. Materials & Design, 2017, 116, 676.
11 Dong G B, Li X C, Zhao J X, et al. Materials Today Communications, 2020, 24, 101332.
12 Cristiano F, Matej B, Carlos P B, et al. Metals, 2019, 9(5), 557.
13 Wei Q H, Xiong J, Sun S, et al. Scientia Sinica(Technologica), 2021, 51(6), 722 (in Chinese).
魏清华, 熊杰, 孙升, 等. 中国科学:技术科学, 2021, 51(6), 722.
14 Wang J H, Sun S, He Y L, et al. Scientia Sinica(Technologica), 2019, 49(10), 1148 (in Chinese).
王家豪, 孙升, 何燕霖, 等. 中国科学:技术科学, 2019, 49(10), 1148.
15 Ghosh G, Olson G B. Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, 2001, 32(3), 455.
16 Farina A B, Mesquita R A, Goldenstein H, et al. Solid State Phenomena, 2011, 172-174, 1171.
17 Geng X X, Wang H, Ullah A, et al. JOM, 2020, 72(11), 3926.
18 Deng Z H, Yin H Q, Jiang X, et al. International Journal of Minerals, Metallurgy and Materials, 2020, 27(3), 362.
19 Paiva A P, Paiva E J, Ferreira J R, et al. The International Journal of Advanced Manufacturing Technology, 2009, 43(7-8), 631.
20 Huang X Y, Wang H, Xue W H, et al. Journal of Alloys and Compounds, 2020, 823, 153694.
21 Huang X Y, Wang H, Xue W H, et al. Computational Materials Science, 2020, 171, 109282.
22 Zhang M, Hua F A, Zhao W. Materials Reports, 2021, 35(S1), 331 (in Chinese).
张猛, 花福安, 赵巍. 材料导报, 2021, 35(S1), 331.
23 Dai D B, Xu T, Wei X, et al. Computational Materials Science, 2020, 175, 109618.
24 Bobbitt N S, Snurr R Q. Molecular simulation, 2019, 45(14-15), 1069.
25 Qiao L, Zhu J C, Wang Y. Materials Letters, 2021, 293, 129645.
26 Wang Y, Qiao G. Journal of Chongqing University of Technology (Natural Science), 2022, 36(4), 243 (in Chinese).
王云润, 乔高秀. 重庆理工大学学报(自然科学), 2022, 36(4), 243.
27 Torres-Serra J, Rodríguez-Ferran A, Romero E. Powder Technology, 2021, 378, 288.
28 Li Z X, Wu D Z, Yu T Y. Journal of Manufacturing Science and Engineering, 2019, 141(3), 31003.
29 Zheng Y J, Liang X B, Zhang Q, et al. Materials Reports, 2021, 35(8), 8207 (in Chinese).
郑玉杰, 梁鑫斌, 张起, 等. 材料导报, 2021, 35(8), 8207.
30 Ashraf M, Zaman M, Ahmed M. Procedia Computer Science, 2018, 132, 1021.
31 Zhou X, Zhang Z, Yang H, et al. Journal of Chongqing University of Technology (Natural Science), 2022, 36(9), 266 (in Chinese).
周晓霞, 张治飞, 杨赫祎, 等. 重庆理工大学学报(自然科学), 2022, 36(9), 266.
32 Cramer S, Kampouridis M, Freitas A A, et al. Expert Systems with Applications, 2017, 85, 169.
33 Liu Z X, Li M Y, Weng Y W, et al. Composites Part B: Engineering, 2020, 193, 108018.
34 Heung B, Bulmer C E, Schmidt M G. Geoderma, 2014, 214, 141.
35 Diamantidis N A, Karlis D, Giakoumakis E A. Artificial Intelligence, 2000, 116(1), 1.
36 Jiang X, Yin H Q, Zhang C, et al. Computational Materials Science, 2018, 143, 295.
37 Hayajneh M T, Hassan A M, Mayyas A T. Journal of Alloys and Compounds, 2009, 478(1-2), 559.
38 Alajmi M S, Almeshal A M. Materials, 2020, 13(21), 4952.
39 Gao Z Y, Kang Y, Li Y S. Materials Research Express, 2018, 5(4), 46520.
40 Jiang X, Yin H Q, Zhang C, et al. Computational Materials Science, 2018, 143, 295.
41 Wu J J, Xu Z H, Qiao H C, et al. Materials Letters, 2021, 297, 129970.
42 Nguyen T A, Ly H B, Mai H V T, et al. Advances in Materials Science and Engineering, 2020, 2020, 1.
43 Geng X X, Wang H, Xue W H, et al. Computational Materials Science, 2020, 171, 109235.
44 Wada S. Journal of the Physical Society of Japan, 2007, 76(9), 94004.
45 Diao Y P, Yan L C, Gao K W. Journal of Materials Science & Technology, 2022, 109, 86.
46 Rodgers J L, Nicewander W A. The American Statistician, 2012, 42(1), 59.

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