Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys
文献类型：期刊论文
| 作者 | Tian, Xiaohua1; Shi, Dingding1; Zhang, Kun2,3; Li, Hongxing2; Zhou, Liwen1; Ma, Tianyou4; Wang, Cheng4; Wen, Qinlong5; Tan, Changlong2 |
| 刊名 | COMPUTATIONAL MATERIALS SCIENCE
 |
| 出版日期 | 2022-12-01 |
| 卷号 | 215页码:7 |
| ISSN号 | 0927-0256 |
| 关键词 | Ferromagnetic shape memory alloys Martensitic transformation temperature Machine learning NiMnSn-based alloys XGBRegressor |
| DOI | 10.1016/j.commatsci.2022.111811 |
| 通讯作者 | Zhang, Kun(kunzhang@hrbust.edu.cn) ; Tan, Changlong(changlongtan@hrbust.edu.cn) |
| 英文摘要 | Martensitic transformation temperature (TM) of NiMnSn-based ferromagnetic shape memory alloys (FSMAs) is crucial to identifying the operating range of an application. From a materials design point of view, an efficient method that can predict the TM accurately should be strongly pursued, to meet various applications with different operating temperatures. In this paper, we demonstrate that machine learning (ML) can rapidly and accurately predict the TM in NiMnSn-based FSMAs. We evaluate the performance of four machine learning models, including Random Forest Regressor (RFR), Support Vector Regression (SVR), Linear Regression (LR), and XGBRegressor (XGBR) model. Three important features of Numa , Arc , and avg Ven are selected as the optimal feature combination for building the model. Moreover, to ensure the best generalization ability of the model, multiple methods of cross-validation (Leave-One-Out Cross-Validation, 3-fold Cross-Validation, and 5-fold Cross -Validation) are used. Finally, the XGBR model exhibits the best performance for predicting the TM (R2 = 0.903 and RMSE = 5.4, R25f = 0.869 and R23f = 0.838). The results of small deviation and variance proven that the XGBR model, proposed in this work, is suitable to be used to predict the TM of unknown NiMnSn-based FSMAs. This work is expected to promote the targeted design of FSMAs. |
| 资助项目 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; [51971085] ; [51871083] ; [52001101] ; [52271172] ; [2021M693229] |
| WOS研究方向 | Materials Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000870259700006 |
| 资助机构 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation |
| 源URL | [http://ir.imr.ac.cn/handle/321006/176361]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Zhang, Kun; Tan, Changlong |
| 作者单位 | 1.Harbin Univ Sci & Technol, Sch Elect & Elect Engn, Harbin 150080, Peoples R China 2.Harbin Univ Sci & Technol, Sch Mat Sci & Chem Engn, Harbin 150040, Peoples R China 3.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 4.Harbin Univ Sci & Technol, Sch Sci, Harbin 150080, Peoples R China 5.Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xian 710072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tian, Xiaohua,Shi, Dingding,Zhang, Kun,et al. Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys[J]. COMPUTATIONAL MATERIALS SCIENCE,2022,215:7. |
| APA | Tian, Xiaohua.,Shi, Dingding.,Zhang, Kun.,Li, Hongxing.,Zhou, Liwen.,...&Tan, Changlong.(2022).Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys.COMPUTATIONAL MATERIALS SCIENCE,215,7. |
| MLA | Tian, Xiaohua,et al."Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys".COMPUTATIONAL MATERIALS SCIENCE 215(2022):7. |
入库方式： OAI收割
来源：金属研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。