Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning

Tungsten Inert Gas welding is dependent on human supervision, it has an emphasis on visual assessment, and it is performed in a controlled environment, making it suitable for automation. This study designs a system for assessing the tungsten inert gas welding quality with the potential of application in real-time. The system uses images in the visible spectrum paired with the state-of-the-art approach for image classification. The welding images represent the weld pool in visible spectra balanced using high dynamic range technology to offset the powerful arc light. The study trains models on a new tungsten inert gas welding dataset, leveraging the state-of-the-art machine learning research, establishing a correlation between the aspect of the weld pool and surrounding area and the weld quality, similar to an operator's assessment.