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

文献类型：期刊论文

作者Yue, Fengli1; Sha, Zhuo1; Sun, Hongyun1; Chen, Dayong2; Liu, Jinsong1,2
刊名APPLIED SCIENCES-BASEL
出版日期2024-12-01
卷号14期号:23页码:23
关键词copper tube drawing ultrasonic detection wall thickness unevenness particle swarm optimization neural network
DOI10.3390/app142311203
通讯作者Yue, Fengli(flyue@163.com)
英文摘要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.
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
语种英语
WOS记录号WOS:001376224100001
出版者MDPI
资助机构Liaoning Provincial Department of Education ; Foundation Project - Science and Technology Plan Project of Chang Zhou
源URL  
专题金属研究所_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.

入库方式： OAI收割

来源：金属研究所

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