| Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys | |
| Tian, Xiaohua1; Shi, Dingding1; Zhang, Kun2,3; Li, Hongxing2; Zhou, Liwen1; Ma, Tianyou4; Wang, Cheng4; Wen, Qinlong5; Tan, Changlong2 | |
| Corresponding Author | Zhang, Kun(kunzhang@hrbust.edu.cn) ; Tan, Changlong(changlongtan@hrbust.edu.cn) |
| 2022-12-01 | |
| Source Publication | COMPUTATIONAL MATERIALS SCIENCE
 |
| ISSN | 0927-0256 |
| Volume | 215Pages:7 |
| Abstract | 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. |
| Keyword | Ferromagnetic shape memory alloys Martensitic transformation temperature Machine learning NiMnSn-based alloys XGBRegressor |
| Funding Organization | National Natural Science Foundation of China ; China Postdoctoral Science Foundation |
| DOI | 10.1016/j.commatsci.2022.111811 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; [51971085] ; [51871083] ; [52001101] ; [52271172] ; [2021M693229] |
| WOS Research Area | Materials Science |
| WOS Subject | Materials Science, Multidisciplinary |
| WOS ID | WOS:000870259700006 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/176362 |
| Collection | 中国科学院金属研究所 |
| Corresponding Author | Zhang, Kun; Tan, Changlong |
| Affiliation | 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 |
| Recommended Citation 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. |
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