Toward super-clean bearing steel by a novel physical-data integrated design strategy

doi:10.1016/j.matdes.2025.113629

IMR OpenIR

	Toward super-clean bearing steel by a novel physical-data integrated design strategy
	Guan, Jian 1,2; Liu, Guolei 1,3; Hu, Wenguang 1,3; Liu, Hongwei 1; Fu, Paixian 1; Cao, Yanfei 1; Liu, Dong-Rong 2; Li, Dianzhong 1
通讯作者	Cao, Yanfei(yfcao10s@imr.ac.cn) ; Liu, Dong-Rong(dongrongliu@hrbust.edu.cn)
	2025-02-01
发表期刊	MATERIALS & DESIGN
ISSN	0264-1275
卷号	250 页码:15
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.
关键词	Vacuum arc remelting (VAR) Inclusions Multi-phase model Genetic algorithm (GA) Physical-data integrated design strategy
资助者	National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
DOI	10.1016/j.matdes.2025.113629
收录类别	SCI
语种	英语
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
WOS类目	Materials Science, Multidisciplinary
WOS记录号	WOS:001403683700001
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/180446
专题	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.