• search hit 13 of 173
Back to Result List

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.

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author: Ivan Kraljevski, Frank Duckhorn, Martin Barth, Constanze Tschöpe, Frank Schubert, Matthias WolffORCiD
DOI:https://doi.org/10.1109/SENSORS47087.2021.9639864
ISBN:978-1-7281-9501-8
Title of the source (English):IEEE SENSORS 2021, Conference Proceedings, Oct 31- Nov 4, Sydney, Australia
Publisher:IEEE
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2021
Number of pages:4
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Kommunikationstechnik
Einverstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.