Application of convolutional neural networks for surface discontinuities detection in shielded metal arc welding process

Detecting surface discontinuities in welds is essential to ensure the structural integrity of welded elements. This study addresses the limitations of manual visual inspection in shielded metal arc welding by applying convolutional neural networks for automated discontinuities detection. A specific image dataset of discontinuities on Shielded Metal Arc Welding weld seams was developed through controlled experiments with various electrode types and welder experience levels, resulting in 3,000 images. The YOLOv7 architecture was trained and evaluated on this dataset, achieving a precision of 97% and mAP@0.5 of 94%. Results showed that increasing the dataset size and training periods significantly improved detection performance, with optimal accuracy observed around 250–300 epochs. The model demonstrated robustness to moderate variations in image aspect ratio and generalization capabilities to an external dataset. This paper presents an approach for detecting SMAW weld surface discontinuities, offering a reliable and efficient alternative to manual inspection and contributing to the advancement of intelligent welding quality control systems.