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Abstract This review paper examines the application and challenges of machine learning (ML) in intelligent welding processes within the automotive industry, focusing on resistance spot welding (RSW) and laser welding. RSW is predominant in body-in-white assembly, while laser welding is critical for electric vehicle battery packs due to its precision and compatibility with dissimilar materials. The paper categorizes ML applications into three key areas: sensing, in-process decision-making, and post-process optimization. It reviews supervised learning models for defect detection and weld quality prediction, unsupervised learning for feature extraction and data clustering, and emerging generalizable ML approaches like transfer learning and federated learning that enhance adaptability across different manufacturing conditions. Additionally, the paper highlights the limitations of current ML models, particularly regarding generalizability when moving from lab environments to real-world production, and discusses the importance of adaptive learning techniques to address dynamically changing conditions. Case studies like virtual sensing, defect detection in RSW, and optimization in laser welding illustrate practical applications. The paper concludes by identifying future research directions to improve ML adaptability and robustness in high-variability manufacturing environments, aiming to bridge the gap between experimental ML models and real-world implementation in automotive welding. 

Abstract Laser keyhole welding of dissimilar metals has been widely used in industrial applications. One critical challenge for this process is the formation of intermetallic compounds (IMCs) that undermine the electrical and mechanical properties of the joints. Compared with the commonly used linear contours, welding with spiral contours can provide larger areas of joining and hence higher allowable loading. This can be particularly useful for certain applications. In this research, laser welding experiments with different spiral contours were performed, and the chemical composition, microstructure, and mechanical properties of the joints were characterized. Three spiral distances were used in the experiments. As the spiral distance was changed from 0.1 mm to 0.3 mm and 0.5 mm, the average Cu concentration in the upper region of the joints was decreased, lower amounts of IMCs were found in the joints, and the joints were capable of sustaining higher mechanical loading.