Friction Stir Welding (FSW) has emerged as a revolutionary solid-state joining process widely used in aerospace, automotive, and marine industries due to its ability to produce high-strength joints with minimal defects. However, to fully leverage its potential, there is a growing need to optimise FSW equipment for improved performance, reliability, and productivity. This study explores the development and integration of intelligent optimisation strategies into FSW equipment design and operation. Key focus areas include real-time process monitoring, adaptive control systems, tool and fixture design enhancement, and thermal management. Advanced techniques such as machine learning, sensor fusion, and data analytics are employed to enable predictive maintenance, dynamic parameter adjustment, and quality assurance. The proposed intelligent framework aims to minimise energy consumption, reduce tool wear, and improve weld quality, ultimately enhancing manufacturing throughput. The findings demonstrate that smart optimisation not only increases equipment efficiency but also sets a foundation for the future of automated, intelligent FSW systems in Industry 4.0 environments.

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