Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers
- This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.
| Author: | Tobias Arndt, Max ConzenORCiD, Ingo ElsenORCiD, Alexander FerreinORCiD, Oskar Galla, Hakan Köse, Stefan Schiffer, Matteo Tschesche |
|---|---|
| DOI: | https://doi.org/10.1145/3594806.3596558 |
| ISBN: | 9798400700699 |
| Parent Title (English): | PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments |
| Publisher: | ACM |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Completion: | 2023 |
| Date of the Publication (Server): | 2023/09/11 |
| Tag: | Anomaly detection; Datasets; Neural networks; Process optimization; Quality control |
| First Page: | 535 |
| Last Page: | 542 |
| Note: | PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece, July 5 - 7, 2023 |
| Link: | https://doi.org/10.1145/3594806.3596558 |
| Zugriffsart: | campus |
| Institutes: | FH Aachen / Fachbereich Elektrotechnik und Informationstechnik |
| collections: | Verlag / ACM |
