Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization

doi:10.1016/j.jmrt.2024.10.225

IMR OpenIR

	Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization
	Li, Yu 1; Zhao, Jingxiao 1; Li, Xiucheng 1; Xing, Zhao 2; Duan, Qiqiang 3; Liang, Xiaojun 2; Wang, Xuemin 1
通讯作者	Li, Xiucheng(xiuchengli@ustb.edu.cn) ; Xing, Zhao(xingzhao@baosteel.com) ; Wang, Xuemin(wxm@mater.ustb.edu.cn)
	2024-11-01
发表期刊	JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
ISSN	2238-7854
卷号	33 页码:6494-6507
摘要	Machine learning (ML) approaches have recently been increasingly employed to establish quantitative relationships between material composition, processing, microstructure, and properties. However, the complexities of microstructure pose challenges for straightforward modeling, thereby complicating research efforts. This study introduces a series of multi-parametric quantification methods based on Electron Backscatter Diffraction (EBSD) data tailored to the microstructural characteristics of low-alloy steels. These methods include quantification of boundary densities across various misorientation angles, distinct types of boundaries, and geometrically necessary dislocation densities. Through thermomechanical simulation and micro-tensile testing of low-alloy steels, data on yield and ultimate tensile strengths were obtained, alongside EBSD-based extraction of microstructure characteristics. Several ML methods, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost), were utilized to predict yield strength (YS) and ultimate tensile strength (UTS) using the aforementioned microstructural features. The GBDT model outperformed other algorithms, demonstrating high accuracy in predicting YS, UTS, and elongation. The model achieved an Mean Squared Error (MSE) of 972.18, an Mean Absolute Error (MAE) of 24.75 and an Coefficient of Determination (R2) of 0.864 for YS, and an MSE of 812.28, an MAE of 22.87 and an R2 of 0.823 for UTS. These results confirm GBDT's effectiveness in predicting mechanical properties from microstructural data.This successful integration of ML with multi-parametric description microstructural features underscores its potential in facilitating material design and development processes.
关键词	Machine learning EBSD Microstructure Mechanical properties Multi-parametric description
资助者	Baosteel Industrial Brain Project
DOI	10.1016/j.jmrt.2024.10.225
收录类别	SCI
语种	英语
资助项目	Baosteel Industrial Brain Project
WOS研究方向	Materials Science ; Metallurgy & Metallurgical Engineering
WOS类目	Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
WOS记录号	WOS:001355221700001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/191411
专题	中国科学院金属研究所
通讯作者	Li, Xiucheng; Xing, Zhao; Wang, Xuemin
作者单位	1.Univ Sci & Technol Beijing, Collaborat Innovat Ctr Steel Technol, 30 Xueyuan Rd, Beijing 100083, Peoples R China 2.Cent Res Inst Baosteel, 889 Fujin Rd, Shanghai 201999, Peoples R China 3.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Li, Yu,Zhao, Jingxiao,Li, Xiucheng,et al. Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization[J]. JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T,2024,33:6494-6507.
APA	Li, Yu.,Zhao, Jingxiao.,Li, Xiucheng.,Xing, Zhao.,Duan, Qiqiang.,...&Wang, Xuemin.(2024).Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization.JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T,33,6494-6507.
MLA	Li, Yu,et al."Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization".JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 33(2024):6494-6507.