Anomaly detection in formed sheet metals using convolutional autoencoders

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschungbegutachtet

Authors

Autoencoders are used to compress data and learn how to reliably reconstruct it. Through comparing the reconstruction error with a set threshold, they are able to detect anomalies in unseen datasets where the data does not quite match the reconstructed input samples. In this work, we attempt to investigate the use of convolutional autoencoders in the field of visual quality inspection, where images of formed sheet metals from a real production line are inspected for the occurrence of cracks and wrinkle formation. This approach tackles the problem of needing enough defective samples to attain reliable detection accuracies
OriginalspracheEnglisch
ZeitschriftProcedia CIRP
Jahrgang93
Seiten (von - bis)1281-1285
Anzahl der Seiten5
ISSN2212-8271
DOIs
PublikationsstatusErschienen - 01.01.2020
Veranstaltung53rd CIRP Conference on Manufacturing Systems - CMS 2020 - Chicago, USA / Vereinigte Staaten
Dauer: 01.07.202003.07.2020
Konferenznummer: 53

DOI