{"title":"The role of force and torque in friction stir welding: A detailed review","authors":"Mostafa Akbari , Milad Esfandiar , 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.