A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization
Recent advancements in artificial intelligence (AI) technologies have expanded their applications across various industrial environments, particularly in the field of Friction Stir Welding (FSW), a relatively modern manufacturing technique. AI techniques are primarily employed for modeling, monitori...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Journal of Advanced Joining Processes |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666330925000378 |
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| author | Mostafa Akbari Ezatollah Hassanzadeh Yaghuob Dadgar Asl Amirhossein Moghanian |
| author_facet | Mostafa Akbari Ezatollah Hassanzadeh Yaghuob Dadgar Asl Amirhossein Moghanian |
| author_sort | Mostafa Akbari |
| collection | DOAJ |
| description | Recent advancements in artificial intelligence (AI) technologies have expanded their applications across various industrial environments, particularly in the field of Friction Stir Welding (FSW), a relatively modern manufacturing technique. AI techniques are primarily employed for modeling, monitoring, optimization, and management of complex systems influenced by multiple parameters within industrial processes. This study systematically reviews and evaluates commonly utilized AI techniques in FSW, highlighting their effectiveness, accuracy, and comparative advantages. The discussion is organized into three distinct sections, each focusing on the critical roles of AI and machine learning (ML) in FSW. The first section addresses process prediction, showcasing how AI techniques predict welding outcomes using historical data and process parameters, which enhances decision-making prior to actual implementation. The second section examines process control, emphasizing how AI systems enable real-time monitoring and adaptive control of the welding process. This functionality allows for immediate parameter adjustments, thus significantly improving weld consistency and quality by minimizing defects. Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. By adopting this structured approach, the review articulates the comprehensive benefits of integrating AI into the friction stir welding process, ultimately contributing to enhanced joint quality and improved operational efficiency. |
| format | Article |
| id | doaj-art-1964e24908554b14b2930de2f41e7c99 |
| institution | Kabale University |
| issn | 2666-3309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Advanced Joining Processes |
| spelling | doaj-art-1964e24908554b14b2930de2f41e7c992025-08-20T03:25:59ZengElsevierJournal of Advanced Joining Processes2666-33092025-06-011110031610.1016/j.jajp.2025.100316A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimizationMostafa Akbari0Ezatollah Hassanzadeh1Yaghuob Dadgar Asl2Amirhossein Moghanian3Department of Mechanical Engineering, National University of Skills (NUS), Tehran, Iran; Corresponding authors.Department of Mechanical Engineering, National University of Skills (NUS), Tehran, Iran; Corresponding authors.Department of Mechanical Engineering, National University of Skills (NUS), Tehran, IranDepartment of Materials Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, IranRecent advancements in artificial intelligence (AI) technologies have expanded their applications across various industrial environments, particularly in the field of Friction Stir Welding (FSW), a relatively modern manufacturing technique. AI techniques are primarily employed for modeling, monitoring, optimization, and management of complex systems influenced by multiple parameters within industrial processes. This study systematically reviews and evaluates commonly utilized AI techniques in FSW, highlighting their effectiveness, accuracy, and comparative advantages. The discussion is organized into three distinct sections, each focusing on the critical roles of AI and machine learning (ML) in FSW. The first section addresses process prediction, showcasing how AI techniques predict welding outcomes using historical data and process parameters, which enhances decision-making prior to actual implementation. The second section examines process control, emphasizing how AI systems enable real-time monitoring and adaptive control of the welding process. This functionality allows for immediate parameter adjustments, thus significantly improving weld consistency and quality by minimizing defects. Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. By adopting this structured approach, the review articulates the comprehensive benefits of integrating AI into the friction stir welding process, ultimately contributing to enhanced joint quality and improved operational efficiency.http://www.sciencedirect.com/science/article/pii/S2666330925000378FSWMachine learningMonitoringModellingArtificial intelligence |
| spellingShingle | Mostafa Akbari Ezatollah Hassanzadeh Yaghuob Dadgar Asl Amirhossein Moghanian A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization Journal of Advanced Joining Processes FSW Machine learning Monitoring Modelling Artificial intelligence |
| title | A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization |
| title_full | A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization |
| title_fullStr | A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization |
| title_full_unstemmed | A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization |
| title_short | A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization |
| title_sort | comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring modelling and process optimization |
| topic | FSW Machine learning Monitoring Modelling Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2666330925000378 |
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