Non Destructive

Non-destructive testing (NDT) focuses on evaluating materials and structures without causing damage, primarily aiming for efficient and accurate defect detection. Current research emphasizes automated NDT using machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), ResNets, and generative models, often coupled with advanced image processing techniques and robotic systems for data acquisition. These advancements are significantly impacting various fields, improving quality control in manufacturing (e.g., composite materials, metal powders), enhancing structural health monitoring (e.g., concrete, stone masonry), and enabling more efficient and objective analysis in areas like forensic science and medical imaging.