Advancing Friction Stir Welding of Dissimilar Metals Using Sensor-based Monitoring and Machine Learning: A Review
Keywords:
Artificial Intelligence; Dissimilar Metals; Friction Stir Welding (FSW); Machine Learning; Process Optimization; Real-Time Monitoring; SensorsAbstract
Friction Stir Welding (FSW) technology has become a transformative solid-state welding method, particularly suited for dissimilar metals in critical applications, such as lithium-ion battery assemblies. Achieving high-quality joints requires real-time control, adaptability, and optimization capabilities that traditional FSW systems struggle to fulfill. This review offers a detailed examination of recent developments in integrating Artificial Intelligence (AI) into FSW process optimization, with a focus on predicting process outcomes and optimizing parameters by analyzing sensor data. The paper highlights emerging hybrid approaches that combine AI with FSW for enhanced process monitoring, modelling, and control. A PRISMA-based methodology was adopted to identify relevant studies from major databases, including Scopus, Web of Science, IEEE, and ScienceDirect, from 2014 to 2025. A total of 156 articles were initially identified, while 102 relevant studies were selected for detailed analysis. Emphasis was placed on prior work in predictive modelling for weld quality, tool condition monitoring systems, and sensor-based real-time optimization frameworks. Special attention is given to FSW of dissimilar metals, such as Al-Cu, outlining metallurgical challenges and demonstrating data-driven solutions to improve joint strength, electrical conductivity, and corrosion resistance. The review reveals that temperature, force, torque, vibration, and transverse speed are critical monitoring parameters for predicting the mechanical properties of FSW joints, including ultimate tensile strength, yield strength, and hardness. Also, machine learning models, such as Artificial Neural Networks (ANNs), Support Vector Machines, Random Forests (RFs), and Gradient Boosting, have been widely used to predict weld quality and FSW process parameters such as mechanical properties, including ultimate tensile strength, hardness, defect formation, and tool condition. ANN and RF models demonstrated strong predictive performance across multiple studies. Despite these advancements, challenges remain, including limited real-time implementation, inadequate standardized datasets, and insufficient research on dissimilar metal welding. This work suggests the need for improved monitoring, real-time AI systems, and greater use of computer vision for automated weld characterization. Future directions are proposed for developing intelligent FSW systems that leverage deep learning, edge computing, and adaptive feedback control for smart manufacturing.References
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