Weld-CNN: Advancing non-destructive testing with a hybrid deep learning model for weld defect detection

Welding is a critical process in industries such as construction, manufacturing, and automotive, where weld quality directly impacts structural integrity and safety. Traditional manual inspection of weld defects via radiographic testing is time-consuming, subjective, and prone to error, underscoring the need for an automated solution. We propose Weld-CNN, a hybrid convolutional neural network that combines sequential convolutional layers with parallel blocks to effectively extract both low-level and high-level features from X-ray images. Trained on a comprehensive dataset of 24,407 X-ray images covering four weld defect categories (cracks, porosity, lack of penetration, and no defect), Weld-CNN achieved a test accuracy of up to 99.83%. The outstanding performance of Weld-CNN demonstrates its potential as a reliable tool for automated, non-destructive weld defect detection, offering significant improvements in efficiency and quality control over manual methodologies.