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.

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Metadaten
Author:Tobias Arndt, Max Conzen, 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

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