Machine learning-based in-process monitoring for laser deep penetration welding: A survey

In-process monitoring (IPM) of laser deep penetration welding (LDPW) has witnessed a rapid growth in approaches that embrace machine learning algorithms, utilizing raw sensor input to generate various weld quality evaluations, instead of concentrating on thermomechanical modeling that is hypotheses-driven and hence biased by it. Benefitting from the capability to unravel hidden interactions in the complex laser welding process, numerous data-driven IPM methods have been proposed to address different problems in this area. In this survey, we present a comprehensive analysis of both classical and recent studies, covering the unique physical mechanisms, sensing techniques, methodologies, strengths, and limitations of machine learning-based IPM-LDPW systems. We delve into several critical tasks, including mechanical performance prediction, weld penetration estimation, and weld defects detection. Meanwhile, we explore the latest developments in deep learning and how to incorporate these techniques into IPM systems for LDPW. To inspire future research, we outline unresolved challenges and explore potential opportunities and new perspectives for addressing these challenges. •This work presents an overview of Machine Learning-based In-Process Monitoring for Laser Deep Penetration Welding (IPM-LDPW).•Summary of issues encountered in three monitoring tasks, along with an explanation of why certain model inputs are effective.•This paper comprehensively presents the state-of-the-art deep learning methods for IPM-LDPW.•Clear directions for future research at the intersection of machine learning and IPM-LDPW are outlined.