TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Barth, Martin A1 - Tschöpe, Constanze A1 - Schubert, Frank A1 - Wolff, Matthias T1 - Autoencoder-based Ultrasonic NDT of Adhesive Bonds T2 - IEEE SENSORS 2021, Conference Proceedings, Oct 31- Nov 4, Sydney, Australia N2 - 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. Y1 - 2021 UR - https://opus4.kobv.de/opus4-UBICO/frontdoor/index/index/docId/28205 SN - 978-1-7281-9501-8 PB - IEEE ER -