Integration of the AI and ML Approaches for Prediction Analysis in Welding: Review

Welding techniques are presents in almost all industries from automotive, aerospace, building, etc. Despite the fact that the technology behind welding remain simple, it is very complicated to characterize and predict the final mechanics responses without considering all the process of the welding. In particular when we refer to the fatigue as the main constraints are located at these singularities areas. Such challenges include poorly controlled welding parameters and weld geometry, which lead to weld quality problems. Nowadays, with the recent involvement of Artificial intelligence (AI) and Machine Learning (ML) into the industry 4.0, it was just matter of time before it is applied to welding problems. The potential applications of ML and AI in welding are vast, as it should bring more efficient and streamlined compared to what is used to be in the early 90’s. In this review a focus will be done on the recent advanced, and reviews previous investigation on AI applications in welding process control and welding robot control. Quality control of such welds are important when we deal with mass production of sensitive products. The main challenges remain the data acquisition to increase the efficiency of the prediction. Testing are sometime coupled with finite elements simulation for training and testing the ML Perspectives and future challenges regarding the integration and the impact of AI in the welding industry in the era of Industry 4.0.