Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network

doi:10.3390/app142311203

IMR OpenIR

	Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network
	Yue, Fengli 1; Sha, Zhuo 1; Sun, Hongyun 1; Chen, Dayong 2; Liu, Jinsong 1,2
通讯作者	Yue, Fengli(flyue@163.com)
	2024-12-01
发表期刊	APPLIED SCIENCES-BASEL
卷号	14 期号:23 页码:23
摘要	After rolling, TP2 copper tubes exhibit defects such as sawtooth marks, cracks, and uneven wall thickness after joint drawing, which severely affects the quality of the finished copper tubes. To study the effect of drawing process parameters on wall thickness uniformity, an ultrasonic detection platform for measuring the wall thickness of rolled copper tubes was constructed to verify the accuracy of the experimental equipment. Using the detected data, a finite element model of drawn copper tubes was established, and numerical simulation studies were conducted to analyze the influence of parameters such as outer die taper angle, drawing speed, and friction coefficient on drawing force, maximum temperature, average wall thickness, and wall thickness uniformity. To address the problem of the large number of finite element model meshes and low solution efficiency, the wall thickness uniformity was predicted using a radial basis function (RBF) neural network, and parameter optimization was performed using the particle swarm optimization (PSO) algorithm. The research results show that the RBF neural network can accurately predict wall thickness uniformity, and using the PSO optimization algorithm, the best parameter combination can reduce the wall thickness uniformity after drawing in finite element simulation.
关键词	copper tube drawing ultrasonic detection wall thickness unevenness particle swarm optimization neural network
资助者	Liaoning Provincial Department of Education ; Foundation Project - Science and Technology Plan Project of Chang Zhou
DOI	10.3390/app142311203
收录类别	SCI
语种	英语
资助项目	Liaoning Provincial Department of Education ; Foundation Project - Science and Technology Plan Project of Chang Zhou[CQ20220057] ; [LJKMZ20220591]
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001376224100001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/181452
专题	中国科学院金属研究所
通讯作者	Yue, Fengli
作者单位	1.Shenyang Ligong Univ, Automot & Transportat Coll, Shenyang 110159, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shi Changxu Mat Innovat Ctr, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Yue, Fengli,Sha, Zhuo,Sun, Hongyun,et al. Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network[J]. APPLIED SCIENCES-BASEL,2024,14(23):23.
APA	Yue, Fengli,Sha, Zhuo,Sun, Hongyun,Chen, Dayong,&Liu, Jinsong.(2024).Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network.APPLIED SCIENCES-BASEL,14(23),23.
MLA	Yue, Fengli,et al."Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network".APPLIED SCIENCES-BASEL 14.23(2024):23.