Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions

Abstract

In the industry, the robotic gas metal arc welding (GMAW) process has a huge range of applications, including in the automotive sector, construction companies, the shipping industry, and many more. Automatic quality inspection in robotic welding is crucial because it ensures the uniformity, strength, and safety of welded joints without the need for constant human intervention. Detecting defects in real time prevents defective products from reaching advanced production stages, reducing reprocessing costs. In addition, the use of materials is optimized by avoiding defective welds that require rework, contributing to cleaner production. This paper presents a novel dataset of robot GMAW images for experimental purposes, including human-expert segmentation and human knowledge labeling regarding the different errors that may appear in welding. In addition, it tests an automatic segmentation approach for robot GMAW quality assessment. The results presented confirm that automatic segmentation is comparable to human segmentation, guaranteeing a correct welding quality assessment to provide feedback on the robot welding process.