This review provides a detailed analysis of real time welding defect detection systems focusing on the critical integration of advanced sensing technologies with artificial intelligence to enhance welding quality assurance. Traditional post process inspection methods are time consuming costly and fundamentally incompatible with the modern automated manufacturing requirement for real time quality control. This necessitates a shift toward in process monitoring systems that detect defects during the welding operation enabling immediate corrective action. The study evaluates the effectiveness of various sensor technologies including optical electrical acoustic thermal and radiographic sensors in identifying diverse welding defects. It then examines the application of advanced AI techniques for welding defect diagnosis covering specialized models such as convolutional and recurrent neural networks transformer and generative models transfer learning multimodal data fusion and hybrid approaches. The review also discusses key challenges such as data quality acquisition scarcity computational resource limitations and system integration complexity. Finally it highlights promising future research directions including lightweight AI models sophisticated multi sensor fusion strategies and digital twin technologies. These advancements have the potential to improve diagnosis accuracy and truly enable real time defect detection during the welding operation ultimately increasing manufacturing efficiency reducing waste and ensuring the production of safer and more reliable welded structures in critical industrial sectors. • Comprehensive review of sensor technologies for real time welding defect diagnosis. • Analysis of AI-based methods applied to welding anomaly diagnosis. • Discussion of current challenges. • Future directions identified such as lightweight AI models, and adaptive multi-sensor systems. • Industry 4.0 relevance to enhance welding quality and reduce waste.
Zhang et al. (Mon,) studied this question.