Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning
文献类型：期刊论文
| 作者 | Li, Yancheng1; Pang, Jingyu2; Li, Zhen3; Wang, Qing1; Wang, Zhenhua1; Li, Jinlin1; Zhang, Hongwei2; Jiao, Zengbao4; Dong, Chuang1; Liaw, Peter K.5 |
| 刊名 | ACTA MATERIALIA
 |
| 出版日期 | 2025-02-15 |
| 卷号 | 285页码:17 |
| 关键词 | High-entropy superalloys gamma/gamma' microstructural stability Mechanical mechanisms Deformation mechanisms Machine learning |
| ISSN号 | 1359-6454 |
| DOI | 10.1016/j.actamat.2024.120656 |
| 通讯作者 | Wang, Qing(wangq@dlut.edu.cn) ; Zhang, Hongwei(hongweizhang@imr.ac.cn) |
| 英文摘要 | Design of novel superalloys with low density, high strength, and great microstructural stability is a big challenge. This work used an automated machine learning (ML) model to explore high-entropy superalloys (HESAs) with coherent gamma' nanoprecipitates in the FCC-gamma matrix. The database samples were firstly preprocessed via the domain-knowledge before ML. Both autogluon and genetic algorithm methods were applied to establish the relationship between the alloy composition and yield strength and to deal with the optimization problem in ML. Thus, the ML model cannot only predict the strength with a high accuracy (R-2 > 95 %), but also design compositions efficiently with desired property in multi-component systems. Novel HESAs with targeted strengths and densities were predicted by ML and then validated by a series of experiments. It is found that the experimental results are well consistent with the predicted properties, as evidenced by the fact that the designed Ni-5.82Fe-15.34Co-2.53Al-2.99Ti-2.90Nb-15.97Cr-2.50Mo (wt.%) HESA has a yield strength of 1346 MPa at room temperature and 1061 MPa at 1023 K and a density of 7.98 g/cm(3). Moreover, it exhibits superior creep resistance with a rupture lifetime of 149 h under 480 MPa at 1023 K, outperforming most conventional wrought superalloys. Additionally, the coarsening rate of gamma' nanoprecipitates in these alloys is extremely slow at 1023 K, showing a prominent microstructural stability. The strengthening and deformation mechanisms were further discussed. This framework provides a new pathway to realize the property-oriented composition design for high-performance complex alloys via ML. |
| 资助项目 | National Natural Science Foundation of China[U24A2024] ; National Natural Science Foundation of China[52171152] ; Research Grant Council of Hong Kong[15227121] ; National Science Foundation[DMR -1611180] ; National Science Foundation[1809640] ; National Science Foundation[2226508] ; US Army Research Office[W911NF-13-1-0438] ; US Army Research Office[W911NF-19-2-0049] |
| WOS研究方向 | Materials Science ; Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001394713900001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Foundation of China ; Research Grant Council of Hong Kong ; National Science Foundation ; US Army Research Office |
| 源URL |  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Wang, Qing; Zhang, Hongwei |
| 作者单位 | 1.Dalian Univ Technol, Engn Res Ctr High Entropy Alloy Mat Liaoning Prov, Sch Mat Sci & Engn, Dalian 116024, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shi changxu Innovat Ctr Adv Mat, Shenyang 110016, Peoples R China 3.Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China 4.Hong Kong Polytech Univ, Res Inst Adv Mfg, Dept Mech Engn, Hong Kong, Peoples R China 5.Univ Tennessee, Dept Mat Sci & Engn, Knoxville, TN 37996 USA |
| 推荐引用方式 GB/T 7714 | Li, Yancheng,Pang, Jingyu,Li, Zhen,et al. Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning[J]. ACTA MATERIALIA,2025,285:17. |
| APA | Li, Yancheng.,Pang, Jingyu.,Li, Zhen.,Wang, Qing.,Wang, Zhenhua.,...&Liaw, Peter K..(2025).Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning.ACTA MATERIALIA,285,17. |
| MLA | Li, Yancheng,et al."Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning".ACTA MATERIALIA 285(2025):17. |
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。