Machine learning techniques in monitoring and controlling friction stir welding process: a critical review
Abstract The friction stir welding (FSW) technique is a solid-state joining method that presents numerous advantages compared to conventional welding techniques, including enhanced mechanical joint efficiency and the ability to join challenging materials such as aluminium and magnesium. FSW is a sop...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07133-8 |
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| author | Bhardwaj Kulkarni Saurabh Tayde Yashwant Chapke Swapnil Vyavahare Avinash Badadhe |
| author_facet | Bhardwaj Kulkarni Saurabh Tayde Yashwant Chapke Swapnil Vyavahare Avinash Badadhe |
| author_sort | Bhardwaj Kulkarni |
| collection | DOAJ |
| description | Abstract The friction stir welding (FSW) technique is a solid-state joining method that presents numerous advantages compared to conventional welding techniques, including enhanced mechanical joint efficiency and the ability to join challenging materials such as aluminium and magnesium. FSW is a sophisticated process characterized by the interaction of various parameters during welding. Inadequate selection of these parameters can lead to defects such as void, tunnel, flash, and other surface and subsurface issues, tool failure, and compromised joint strength. The quality of the weld is essential for ensuring the durability and safety of the final product. Establishing optimal process parameters requires extensive trial and error, hindering productivity. This challenge can be effectively addressed by leveraging recent advancements in the manufacturing industry, particularly the 'Machine Learning: Industry 4.0 Revolution' approach. Machine learning aims to enhance outcomes by integrating digital and physical processes, facilitating efficient monitoring and management of machine performance. By employing machine learning techniques, the FSW process can become more cost-effective through optimizing process parameters, early detection of defects and tool failures, reduction of waste, and attainment of superior joint properties, all while minimizing the need for extensive trial and error. This review article critically explores how machine learning can optimize process parameters, identify and rectify defects, predict and manage tool failures, and control corrosion rates to achieve high-quality FSW joints.Using causative variable model accuracy in predicting tool failure increases from 87 to 98.1% as compared to raw FSW process variables. |
| format | Article |
| id | doaj-art-dfa31a1b7c174dc2a4792e17ec425cc4 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-dfa31a1b7c174dc2a4792e17ec425cc42025-08-20T03:22:03ZengSpringerDiscover Applied Sciences3004-92612025-05-017614410.1007/s42452-025-07133-8Machine learning techniques in monitoring and controlling friction stir welding process: a critical reviewBhardwaj Kulkarni0Saurabh Tayde1Yashwant Chapke2Swapnil Vyavahare3Avinash Badadhe4Practical Vision ConsultantsStudent Progression and Industry Relations, JSPM’s Rajarshi Shahu College of EngineeringDepartment of Automation and Robotics, JSPM’s Rajarshi Shahu College of EngineeringDepartment of Automation and Robotics, JSPM’s Rajarshi Shahu College of EngineeringDepartment of Automation and Robotics, JSPM’s Rajarshi Shahu College of EngineeringAbstract The friction stir welding (FSW) technique is a solid-state joining method that presents numerous advantages compared to conventional welding techniques, including enhanced mechanical joint efficiency and the ability to join challenging materials such as aluminium and magnesium. FSW is a sophisticated process characterized by the interaction of various parameters during welding. Inadequate selection of these parameters can lead to defects such as void, tunnel, flash, and other surface and subsurface issues, tool failure, and compromised joint strength. The quality of the weld is essential for ensuring the durability and safety of the final product. Establishing optimal process parameters requires extensive trial and error, hindering productivity. This challenge can be effectively addressed by leveraging recent advancements in the manufacturing industry, particularly the 'Machine Learning: Industry 4.0 Revolution' approach. Machine learning aims to enhance outcomes by integrating digital and physical processes, facilitating efficient monitoring and management of machine performance. By employing machine learning techniques, the FSW process can become more cost-effective through optimizing process parameters, early detection of defects and tool failures, reduction of waste, and attainment of superior joint properties, all while minimizing the need for extensive trial and error. This review article critically explores how machine learning can optimize process parameters, identify and rectify defects, predict and manage tool failures, and control corrosion rates to achieve high-quality FSW joints.Using causative variable model accuracy in predicting tool failure increases from 87 to 98.1% as compared to raw FSW process variables.https://doi.org/10.1007/s42452-025-07133-8Machine learning (ML)Friction stir welding (FSW)Process optimizationDefects and corrosion rateTool failureIntelligent manufacturing |
| spellingShingle | Bhardwaj Kulkarni Saurabh Tayde Yashwant Chapke Swapnil Vyavahare Avinash Badadhe Machine learning techniques in monitoring and controlling friction stir welding process: a critical review Discover Applied Sciences Machine learning (ML) Friction stir welding (FSW) Process optimization Defects and corrosion rate Tool failure Intelligent manufacturing |
| title | Machine learning techniques in monitoring and controlling friction stir welding process: a critical review |
| title_full | Machine learning techniques in monitoring and controlling friction stir welding process: a critical review |
| title_fullStr | Machine learning techniques in monitoring and controlling friction stir welding process: a critical review |
| title_full_unstemmed | Machine learning techniques in monitoring and controlling friction stir welding process: a critical review |
| title_short | Machine learning techniques in monitoring and controlling friction stir welding process: a critical review |
| title_sort | machine learning techniques in monitoring and controlling friction stir welding process a critical review |
| topic | Machine learning (ML) Friction stir welding (FSW) Process optimization Defects and corrosion rate Tool failure Intelligent manufacturing |
| url | https://doi.org/10.1007/s42452-025-07133-8 |
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