Automated Metallic Weld Defect Detection

Abstract

In several industries, such as manufacturing, construction, and the automotive sector, welding is an essential procedure. Safety and structural integrity are directly impacted by weld quality. Conventional welding inspection techniques result in inconsistencies and inefficiencies because they are labor-intensive, manual, and prone to human error. This study uses convolutional neural networks (CNN) and machine learning (ML) to offer an automated welding fault diagnosis method. Before assigning the weld to one of six categories—Good Weld, Burn Through, Contamination, Misalignment, Lack of Penetration, and Lack of Fusion—the system first confirms whether welding has been done. The model achieves great accuracy in defect identification after being trained on a variety of datasets. This method is appropriate for industrial applications since it increases efficiency, decreases reliance on humans, and improves the accuracy of defect identification by utilizing deep learning techniques.