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Mastering Friction Stir Welding (FSW) With Machine Learning (ML): A Comprehensive Guide to Algorithms and Applications
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Author(s): Raheem Al-Sabur (University of Basrah, Iraq), Akshansh Mishra (Politecnico di Milano, Italy)and Hassanein I. Khalaf (Univeristy of Basrah, Iraq)
Copyright: 2025
Pages: 30
Source title:
Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials
Source Author(s)/Editor(s): Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea), Bikash Ranjan Moharana (Papua New Guinea University of Technology, Papua New Guinea), Steve Korakan Ales (Papua New Guinea University of Technology, Papua New Guinea)and Dillip Kumar Biswal (Aryan Institute of Engineering and Technology, Bhubaneswar, India)
DOI: 10.4018/979-8-3693-7974-5.ch017
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Abstract
This chapter introduces machine learning (ML) in friction stir welding (FSW), a solid-state welding process that has gained significant attention in research and application. The chapter discusses five primary ML methods: artificial neural networks (ANNs), support vector machines (SVM), random forests (RF), particle swarm optimisation (PSO), and convolutional neural networks (CNNs). The chapter emphasizes the successful application of ANNs in optimizing FSW process parameters and predicting tool wear, tensile failure, and fracture positions. CNNs are shown to be effective for microstructure studies and image detection, while SVM is a good tool for FSW process monitoring and temperature control. RF is demonstrated to have good abilities in investigating welding defects and tool monitoring, while PSO is frequently used in FSW welding bead studies. The chapter provides a straightforward methodology for those interested in utilising ML in welding studies, particularly for FSW.
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