Autoencoder-based Ultrasonic NDT of Adhesive Bonds

We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with autoencoders. The models are trained exclusively on the features derived from pulse-echo ultrasonic signals on a specimen with good adhesive bonding and tested on another specimen with artificially added defects. The resulting pseudo-probabilities indicating anomalies are visualized and presented along to the C-scan of the same specimen. As a result, we achieved improved representation of the defects, allowing their automatic and reliable detection.