Hybrid machine learning models for automated classification of weld defects in gas metal arc robotic welding

Abstract The research study explores an advanced approach to classify weld bead defects in a Gas Metal Arc Welding (GMAW) process of robotic welding, contributing to the growing field of intelligent manufacturing and quality control in industrial automation. To precisely categorize six classes of weld bead such as lack of fusion, burn-through, misalignment, lack of penetration, contamination, and good weld, a hybrid machine learning approach that combines Convolutional Neural Networks (CNN) and Vision Transformers (ViT) is utilized. The hybrid model leverages the complementary advantages of CNN for the extraction of localized features and ViT for global contextual awareness, resulting in superior classification performance compared to traditional architectures such as ResNet and conventional CNN models. The performance evaluation of proposed model demonstrates the model's robustness and reliability in diverse operational scenarios. The findings highlight the potential of integrating hybrid deep learning models into industrial automation systems to enhance weld bead defect detection to reduce operational inefficiencies and ensure consistent weld quality in manufacturing process.