改进 RSW 金块直径预测方法:释放多保真度神经网络和迁移学习的力量

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Advances in Manufacturing Pub Date : 2024-06-18 DOI:10.1007/s40436-024-00503-2
Zhong-Jie Yue, Qiu-Ren Chen, Zu-Guo Bao, Li Huang, Guo-Bi Tan, Ze-Hong Hou, Mu-Shi Li, Shi-Yao Huang, Hai-Long Zhao, Jing-Yu Kong, Jia Wang, Qing Liu
{"title":"改进 RSW 金块直径预测方法:释放多保真度神经网络和迁移学习的力量","authors":"Zhong-Jie Yue,&nbsp;Qiu-Ren Chen,&nbsp;Zu-Guo Bao,&nbsp;Li Huang,&nbsp;Guo-Bi Tan,&nbsp;Ze-Hong Hou,&nbsp;Mu-Shi Li,&nbsp;Shi-Yao Huang,&nbsp;Hai-Long Zhao,&nbsp;Jing-Yu Kong,&nbsp;Jia Wang,&nbsp;Qing Liu","doi":"10.1007/s40436-024-00503-2","DOIUrl":null,"url":null,"abstract":"<div><p>This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding (RSW) by leveraging machine learning and transfer learning methods. Initially, low-fidelity (LF) data were obtained through finite element numerical simulation and design of experiments (DOEs) to train the LF machine learning model. Subsequently, high-fidelity (HF) data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques. The accuracy and generalization performance of the models were thoroughly validated. The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials, and provide an effective and valuable method for predicting critical process parameters in RSW.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving RSW nugget diameter prediction method: unleashing the power of multi-fidelity neural networks and transfer learning\",\"authors\":\"Zhong-Jie Yue,&nbsp;Qiu-Ren Chen,&nbsp;Zu-Guo Bao,&nbsp;Li Huang,&nbsp;Guo-Bi Tan,&nbsp;Ze-Hong Hou,&nbsp;Mu-Shi Li,&nbsp;Shi-Yao Huang,&nbsp;Hai-Long Zhao,&nbsp;Jing-Yu Kong,&nbsp;Jia Wang,&nbsp;Qing Liu\",\"doi\":\"10.1007/s40436-024-00503-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding (RSW) by leveraging machine learning and transfer learning methods. Initially, low-fidelity (LF) data were obtained through finite element numerical simulation and design of experiments (DOEs) to train the LF machine learning model. Subsequently, high-fidelity (HF) data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques. The accuracy and generalization performance of the models were thoroughly validated. The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials, and provide an effective and valuable method for predicting critical process parameters in RSW.</p></div>\",\"PeriodicalId\":7342,\"journal\":{\"name\":\"Advances in Manufacturing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s40436-024-00503-2\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s40436-024-00503-2","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0

摘要

本研究提出了一种创新方法,利用机器学习和迁移学习方法准确预测电阻点焊(RSW)中的焊块直径。首先,通过有限元数值模拟和实验设计(DOE)获得低保真(LF)数据,以训练 LF 机器学习模型。随后,通过 RSW 工艺实验收集高保真(HF)数据,并利用迁移学习技术对低保真模型进行微调。模型的准确性和泛化性能得到了全面验证。结果表明,结合不同保真度的数据集并采用迁移学习可以显著提高预测精度,同时最大限度地降低与实验相关的成本,为预测 RSW 的关键工艺参数提供了一种有效且有价值的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Improving RSW nugget diameter prediction method: unleashing the power of multi-fidelity neural networks and transfer learning

This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding (RSW) by leveraging machine learning and transfer learning methods. Initially, low-fidelity (LF) data were obtained through finite element numerical simulation and design of experiments (DOEs) to train the LF machine learning model. Subsequently, high-fidelity (HF) data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques. The accuracy and generalization performance of the models were thoroughly validated. The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials, and provide an effective and valuable method for predicting critical process parameters in RSW.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
期刊最新文献
Grinding defect characteristics and removal mechanism of unidirectional Cf/SiC composites The effect of the slope angle and the magnetic field on the surface quality of nickel-based superalloys in blasting erosion arc machining Study on the mechanism of burr formation in ultrasonic vibration-assisted honing 9Cr18MoV valve sleeve Flexible modification and texture prediction and control method of internal gearing power honing tooth surface ·AI-enabled intelligent cockpit proactive affective interaction: middle-level feature fusion dual-branch deep learning network for driver emotion recognition
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1