Document Type
Article
Publication Date
2-7-2025
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.
Keywords
intelligent welding, laser welding, machine learning, model generalization, quality assurance
Language
English
Publication Title
Welding in the World
Grant
2237242
Rights
© The Author(s) 2025. This is an Open Access work distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Wang, P.E., Ghassemi-Armaki, H., Pour, M. et al. Applicable and generalizable machine learning for intelligent welding in automotive manufacturing. Weld World 69, 1349–1384 (2025). https://doi.org/10.1007/s40194-025-01951-5
Manuscript Version
Final Publisher Version