Classification of Weld Bead Defects Using Convolutional Neural Networks

Welding plays a critical role in industrial manufacturing, defects such as cracks, porosity, and incomplete fusion compromise structural integrity. Traditional inspection techniques, including manual assessment and non-destructive testing (NDT), are labor-intensive and prone to human error. This study presents a comparative study on the visual classification of weld bead defects in Shielded Metal Arc Welding (SMAW) using Convolutional Neural Networks (CNNs). The study used two image datasets: one with image preprocessing techniques applied (sharpening, adaptive contrast adjustment, and Gaussian smoothing), and another without any preprocessing. Both datasets were used to train and evaluate a CNN model under controlled laboratory conditions. The results demonstrate that preprocessing improves model performance, increasing accuracy from 91.66% (without filters) to 98.00% (with filters). A detailed comparison with related works highlights the contribution of this approach, which not only achieves competitive accuracy but also introduces a replicable methodology for dataset preparation and analysis.