A welding seam identification method based on cross-modal perception

Abstract

Purpose As an automatic welding process may experience some disturbances caused by, for example, splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights and diversified welding joints/grooves, precisely identifying the welding seam has a great influence on the welding quality. This paper aims to propose a robust method for identifying this seam based on cross-modal perception. Design/methodology/approach First, after a welding image obtained from a structured-light vision sensor (here laser and vision are integrated into a cross-modal perception sensor) is filtered, in a sufficiently small area, the extended Kalman filter is used to prevent possible disturbances to search for its laser stripe. Second, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Third, this profile is then qualitatively described and matched using a proposed character string method. Findings It is demonstrated that it maintains real time and is clearly superior in terms of accuracy and robustness, though its real-time performance is not the best. Originality/value This paper proposes a robust method for automatically identifying and tracking a welding seam.

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“…Artificial intelligence (AI) and machine learning (ML) technology integration is one of the most important developments in welding robots (Yifei et al, 2018;de Oliveira Evald et al, 2017;Malviya et al, 2018;Huang et al, 2020). The accuracy and effectiveness of welding robots may be considerably increased by using AI and ML, claim Li et al (2019) stated that with the aid of these technologies, the robot might adapt to its experiences and increase the welding quality. A higher-quality weld will emerge from the robot's ability to detect and rectify flaws in real-time thanks to AI and ML.…”