Ultrasonic Non Destructive Testing

Ultrasonic non-destructive testing (NDT) uses high-frequency sound waves to detect internal flaws in materials without causing damage, a crucial technique for ensuring structural integrity in various applications. Current research emphasizes automating NDT through machine learning, particularly using deep convolutional neural networks (CNNs) and generative adversarial networks (GANs) to improve defect detection and localization from ultrasonic images, often augmented with simulated data to address data scarcity. These advancements are improving inspection efficiency, reducing reliance on human expertise, and enabling more comprehensive material characterization, with applications ranging from industrial manufacturing to infrastructure monitoring.