Anomaly detection in formed sheet metals using convolutional autoencoders
Research output: Journal contributions › Conference article in journal › Research › peer-review
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
| Original language | English |
|---|---|
| Journal | Procedia CIRP |
| Volume | 93 |
| Pages (from-to) | 1281-1285 |
| Number of pages | 5 |
| ISSN | 2212-8271 |
| DOIs | |
| Publication status | Published - 01.01.2020 |
| Event | 53rd CIRP Conference on Manufacturing Systems - CMS 2020 - Chicago, United States Duration: 01.07.2020 → 03.07.2020 Conference number: 53 |
Bibliographical note
The authors gratefully acknowledge the financial support given by the Federal Ministry for Economic Affairs and Energy (BMWi) under the Central Innovation Programme for SMEs IM)(Zorfe thopjrectartSPm“”ress on the basis of a decision of the German Bundestag (grant number: ZF4084503GR7).
- Engineering - Artifical intelligence, Deep autoencoders, Process monitoring, Surface quality inspection