Investigation on the Structure Profile of Precise Copper Tube by Three-Roll Planetary Rolling Based on Finite Element Simulation Assisted by Machine Learning

doi:10.1007/s11665-024-10380-2

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	Investigation on the Structure Profile of Precise Copper Tube by Three-Roll Planetary Rolling Based on Finite Element Simulation Assisted by Machine Learning
	Liu, Jin-Song 1,2,3; Sun, Yang-Le 1; Chen, Da-Yong 2,3; Song, Hong-Wu 2; Zhang, Shi-Hong 2; Chen, Chuan-Lai 3
通讯作者	Chen, Da-Yong(dychen15b@imr.ac.cn)
	2024-12-03
发表期刊	JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE
ISSN	1059-9495
页码	14
摘要	In three-roll planetary rolling (TRPR), the tubular billet undergoes large deformation of 90% under the state of compression and shear by the constraint of three rolls. In this process, the microstructures and the properties of the rolled tubular billet will change dramatically because of the dynamic recrystallization induced by the large enough deformation. As it is all known, the obtaining of structure profile with high precision and efficiency becomes the investigation foundation of the microstructure. However, the structure profile of the tubular billet is still difficult to be described exactly because of the complex deformation which will be significantly influenced by plenty of process parameters. Therefore, the finite element method (FEM) was utilized to investigate the influence of the adjustable angles including the inclined angles and the deflection angles on the structure profile of rolled workpieces. Furthermore, the machine learning was subsequently utilized to learn the correlation between the process parameters and the structure profile and provide further prediction. In addition, the theoretical analysis model was established and optimized to reveal the relationship between the process parameters and the axial extension coefficient lambda which is calculated to represent the deformation degree along to the axial direction. It is concluded that the inclined angles and the deflection angles of rolls are known to be the one of key factors affecting the structure profile of rolled tube. The prediction model is established and applied to efficiently estimate the structure profile under any process parameters including different initial size of billets, the inclined angles, and the deflection angles. This investigation will contribute to exact controlling of the microstructure of the copper tube by quantitative means.
关键词	axial extension coefficient finite element method machine learning structure profile three-roll planetary rolling
资助者	Science and Technology Plan Project of Chang Zhou ; Basic Scientific Project of Liaoning Provincial Department of Education
DOI	10.1007/s11665-024-10380-2
收录类别	SCI
语种	英语
资助项目	Science and Technology Plan Project of Chang Zhou[CQ20220057] ; Basic Scientific Project of Liaoning Provincial Department of Education[LJKMZ20220591]
WOS研究方向	Materials Science
WOS类目	Materials Science, Multidisciplinary
WOS记录号	WOS:001368857700001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/181304
专题	中国科学院金属研究所
通讯作者	Chen, Da-Yong
作者单位	1.Shenyang Ligong Univ, Sch Mat Sci & Engn, Shenyang 110159, Peoples R China 2.Chinese Acad Sci, Shi Changxu Innovat Ctr Adv Mat, Inst Met Res, Shenyang 110016, Peoples R China 3.Changzhou Enreach Copper Co Ltd, Changzhou 213149, Peoples R China
推荐引用方式 GB/T 7714	Liu, Jin-Song,Sun, Yang-Le,Chen, Da-Yong,et al. Investigation on the Structure Profile of Precise Copper Tube by Three-Roll Planetary Rolling Based on Finite Element Simulation Assisted by Machine Learning[J]. JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE,2024:14.
APA	Liu, Jin-Song,Sun, Yang-Le,Chen, Da-Yong,Song, Hong-Wu,Zhang, Shi-Hong,&Chen, Chuan-Lai.(2024).Investigation on the Structure Profile of Precise Copper Tube by Three-Roll Planetary Rolling Based on Finite Element Simulation Assisted by Machine Learning.JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE,14.
MLA	Liu, Jin-Song,et al."Investigation on the Structure Profile of Precise Copper Tube by Three-Roll Planetary Rolling Based on Finite Element Simulation Assisted by Machine Learning".JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE (2024):14.