A systematic framework for tackling anomalous pre-welding workpiece postures with regular butt joints based on prototype features


In intelligent welding systems, pre-welding parameter extraction is a foremost technology in upgrading robotic welding and integrating emerging technologies, e.g., digital twins, big data, and cloud manufacturing. However, current workpiece postures still rely on manual judgments based on workers’ experience, which has become one of major issues hindering the further advancement of intelligent welding systems towards mass production. To cope with this issue, aiming at four typical regular butt joints, a systematic tackling framework is proposed and carried out from posture description, posture feature construction, posture metrics to visualization model reconstruction. At a core, the prototype feature is proposed to characterize pre-welding workpiece postures and a series of text characters based on it is introduced to describe various anomalous workpiece postures including concurrent tilt, misalignment, seam variation, and stacking between them. A comprehensive process for constructing prototype features is performed from data acquisition, image processing to key point search, among which the algorithms for extracting differential features of different seam types are integrated. Based on the constructed prototype features, several posture metric parameters are defined, and workpiece posture models can be easily reconstructed. In addition, the good generalizability of the proposed framework for seam types with regular edge and seam features is also discussed. Ultimately, experimental results show that the prototype feature-based posture description of the pre-welding workpiece can efficiently and accurately characterize multiple anomalous postures. © 2023 The Society of Manufacturing Engineers

Journal of Manufacturing Systems
Yuqian Lu
Yuqian Lu
Principle Investigator / Senior Lecturer

My research interests include smart manufacturing systems, industrial AI and robotics.