Quality monitoring of hybrid welding processes: A comprehensive review

Hybrid welding processes have gained significant attention due to their high efficiency and exceptional welding properties. However, there are still significant technological challenges in achieving consistent quality and suppressing welding defects. To overcome this challenge, researchers have focused on the integration of visual analysis techniques, numerical simulation techniques, and advanced technologies such as artificial intelligence/machine learning (AI/ML) and digital twins. This comprehensive review synthesizes current knowledge on quality monitoring in hybrid welding, encompassing an overview of hybrid welding processes, quality assurance, monitoring techniques, key performance indicators, and advancements in monitoring techniques. Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. Notably, the review emphasizes the incorporation of artificial intelligence (AI) and digital twin technologies into quality monitoring frameworks. Artificial intelligence/Machine learning enables real-time analysis of welding parameters and defect detection, while digital twins offer virtual representations of physical welding processes, facilitating predictive maintenance and optimization. The findings underscore the crucial role of sensor technology, AI/ML, and digital twin integration in enhancing defect detection accuracy, improving welded joint quality, and control in hybrid welding. In addition to improving the quality of welded joints, this integration paves the way for further developments in welding technology.