Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization
文献类型：期刊论文
| 作者 | Li, Yu1; Zhao, Jingxiao1; Li, Xiucheng1; Xing, Zhao2; Duan, Qiqiang3; Liang, Xiaojun2; Wang, Xuemin1 |
| 刊名 | JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 33页码:6494-6507 |
| 关键词 | Machine learning EBSD Microstructure Mechanical properties Multi-parametric description |
| ISSN号 | 2238-7854 |
| DOI | 10.1016/j.jmrt.2024.10.225 |
| 通讯作者 | Li, Xiucheng(xiuchengli@ustb.edu.cn) ; Xing, Zhao(xingzhao@baosteel.com) ; Wang, Xuemin(wxm@mater.ustb.edu.cn) |
| 英文摘要 | 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. |
| 资助项目 | Baosteel Industrial Brain Project |
| WOS研究方向 | Materials Science ; Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001355221700001 |
| 出版者 | ELSEVIER |
| 资助机构 | Baosteel Industrial Brain Project |
| 源URL |  |
| 专题 | 金属研究所_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. |
入库方式： OAI收割
来源：金属研究所
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