Abstract Gas metal arc welding (GMAW) of AISI 304 stainless steel is extensively employed in structural and industrial applications due to its corrosion resistance and weldability, yet it remains prone to porosity, lack of fusion, spatter, and pen- etration defects that compromise joint integrity. Weld quality has traditionally been evaluated through visual inspection (VI), a method influenced by inspec- tor skill, fatigue, and environmental conditions. To address these limitations, machine vision (MV) was comparatively evaluated under AWS D1.6 (2017). Six specimens were prepared, one defect-free and five with induced discontinuities. VI was conducted by certified inspectors, while MV used digital cameras, CMY preprocessing, supervised labeling, and contour detection. Validation employed. Confusion matrices and performance metrics. Results showed VI detected major discontinuities but lacked reproducibility. MV consistently identified all induced defects, achieving 0.92 precision, 1.00 recall, and 0.96 F1-score, confirming its robustness as an alternative for weld quality control. Graphical abstract
Torres-Torres et al. (Wed,) studied this question.