The role of force and torque in friction stir welding: A detailed review

IF 3.8 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mostafa Akbari , Milad Esfandiar , Amin Abdollahzadeh
{"title":"The role of force and torque in friction stir welding: A detailed review","authors":"Mostafa Akbari ,&nbsp;Milad Esfandiar ,&nbsp;Amin Abdollahzadeh","doi":"10.1016/j.jajp.2025.100289","DOIUrl":null,"url":null,"abstract":"<div><div>Friction Stir Welding (FSW) is a significant solid-state joining technique for metals and polymers, effectively addressing challenges posed by fusion welding. The application of FSW relies on the development of cost-effective, durable tools that consistently produce high-quality welds. The forces and torque generated during welding are critical to this process, which influence weld integrity, process efficiency, and tool longevity. This review explores methodologies for estimating these parameters—analytical, numerical, and experimental—and discusses measurement techniques, including direct and indirect methods. It also examines variations in forces across different FSW types, such as Conventional FSW, Bobbin Tool FSW, and Stationary Shoulder FSW, emphasizing the differences in their operational mechanics. Additionally, the review highlights how process parameters like tool shape, size, tilt angle, and welding speed can be optimized to enhance performance and investigates the use of force measurements for real-time weld monitoring and defect detection, contributing to the reliability of FSW in industrial applications. The results indicate that the use of force measurement for online monitoring of welding processes, particularly concerning welding defects and overall weld quality, has garnered significant attention in recent years. ​ A notable advancement in this field is the implementation of machine learning tools, which enhance the ability to predict potential weld defects and improve overall weld quality. This innovative approach not only streamlines the monitoring process but also contributes to the evolution of FSW technologies, ensuring higher standards of quality and safety in various applications.</div></div>","PeriodicalId":34313,"journal":{"name":"Journal of Advanced Joining Processes","volume":"11 ","pages":"Article 100289"},"PeriodicalIF":3.8000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Joining Processes","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266633092500010X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Friction Stir Welding (FSW) is a significant solid-state joining technique for metals and polymers, effectively addressing challenges posed by fusion welding. The application of FSW relies on the development of cost-effective, durable tools that consistently produce high-quality welds. The forces and torque generated during welding are critical to this process, which influence weld integrity, process efficiency, and tool longevity. This review explores methodologies for estimating these parameters—analytical, numerical, and experimental—and discusses measurement techniques, including direct and indirect methods. It also examines variations in forces across different FSW types, such as Conventional FSW, Bobbin Tool FSW, and Stationary Shoulder FSW, emphasizing the differences in their operational mechanics. Additionally, the review highlights how process parameters like tool shape, size, tilt angle, and welding speed can be optimized to enhance performance and investigates the use of force measurements for real-time weld monitoring and defect detection, contributing to the reliability of FSW in industrial applications. The results indicate that the use of force measurement for online monitoring of welding processes, particularly concerning welding defects and overall weld quality, has garnered significant attention in recent years. ​ A notable advancement in this field is the implementation of machine learning tools, which enhance the ability to predict potential weld defects and improve overall weld quality. This innovative approach not only streamlines the monitoring process but also contributes to the evolution of FSW technologies, ensuring higher standards of quality and safety in various applications.
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.10
自引率
9.80%
发文量
58
审稿时长
44 days
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信