Toward super-clean bearing steel by a novel physical-data integrated design strategy
文献类型：期刊论文
| 作者 | Guan, Jian1,2; Liu, Guolei1,3; Hu, Wenguang1,3; Liu, Hongwei1; Fu, Paixian1; Cao, Yanfei1; Liu, Dong-Rong2; Li, Dianzhong1 |
| 刊名 | MATERIALS & DESIGN
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 250页码:15 |
| 关键词 | Vacuum arc remelting (VAR) Inclusions Multi-phase model Genetic algorithm (GA) Physical-data integrated design strategy |
| ISSN号 | 0264-1275 |
| DOI | 10.1016/j.matdes.2025.113629 |
| 通讯作者 | Cao, Yanfei(yfcao10s@imr.ac.cn) ; Liu, Dong-Rong(dongrongliu@hrbust.edu.cn) |
| 英文摘要 | The cleanliness of fabricated ingots is crucial for the quality and properties of bearing steel. To address this issue, a physical-data integrated design strategy was developed to optimize vacuum arc remelting (VAR) parameters, combining numerical simulation, machine learning (ML), and experimental validation. Initially, a multi-phase, multi-physics coupled model was developed to predict the movement and distribution of inclusions during the VAR process. Furthermore, five ML algorithms were utilized to predict the cleanliness assessment score (CAS) based on inclusion size and distribution data from various VAR processing parameters, with gradient boosting regression (GBR) showing the best performance. Finally, a systematic framework based on a genetic algorithm was proposed to select the optimal combination of CAS. Here, the ML-optimized processing parameters comprised current of 4255 A, helium pressure of 0.69 kPa, and melting rate of 2.5 kg/min. Intriguingly, the number density of small inclusions at the center of the ingot decreased by 58.2 % and that of large inclusions reduced by 13.3 %. This was mainly caused by the appropriate maximum flow velocity of 2.6-2.8 cm/s during the steady-state stage of the molten pool. This study highlights a common and novel method for fabricating bearing steel with other superalloys via a physical-data integrated strategy. |
| 资助项目 | National Natural Science Foundation of China[52471053] ; National Natural Science Foundation of China[52321001] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA0410000] |
| WOS研究方向 | Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001403683700001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL |  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Cao, Yanfei; Liu, Dong-Rong |
| 作者单位 | 1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Peoples R China 2.Harbin Univ Sci & Technol, Sch Mat Sci & Chem Engn, 4 Linyuan Rd, Harbin 150040, Peoples R China 3.Univ Sci & Technol China, Sch Mat Sci & Engn, 96 JinZhai Rd, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guan, Jian,Liu, Guolei,Hu, Wenguang,et al. Toward super-clean bearing steel by a novel physical-data integrated design strategy[J]. MATERIALS & DESIGN,2025,250:15. |
| APA | Guan, Jian.,Liu, Guolei.,Hu, Wenguang.,Liu, Hongwei.,Fu, Paixian.,...&Li, Dianzhong.(2025).Toward super-clean bearing steel by a novel physical-data integrated design strategy.MATERIALS & DESIGN,250,15. |
| MLA | Guan, Jian,et al."Toward super-clean bearing steel by a novel physical-data integrated design strategy".MATERIALS & DESIGN 250(2025):15. |
入库方式： OAI收割
来源：金属研究所
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