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 sp...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Kraljevski, Ivan, Duckhorn, Frank, Barth, Martin, Tschöpe, Constanze, Schubert, Frank, Wolff, Matthias
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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, providing a possibility of their automatic and reliable detection.
DOI:10.1109/SENSORS47087.2021.9639864