A data-centric approach to anomaly detection in layer-based additive manufacturing

at - Automatisierungstechnik, vol. 71, pp. 81–89

Abstract

Abstract Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for quality enhancement. A main challenge, however, is absence of labels in this environment. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production. With a use case from additive manufacturing for automotive components we present a deep-learning-based image processing pipeline. We integrate the concept of domain randomisation and synthetic data in the loop that shows promising results for bridging advances in deep learning and its application to real-world, industrial production processes.

Authors 5

1. Alexander Zeiser corresponding Aachen Leiden Institute of Advanced Computer Science

   Leiden University · BMW Group (Germany)

   Affiliation as printed

   BMW Group , Ohmstraße 2, 84030 Landshut , Germany

   Leiden Institute of Advanced Computer Science , Niels Bohrweg 1, 2333 CA Leiden , The Netherlands

2. Bekir Özcan

   BMW Group (Germany)

   Affiliation as printed

   BMW Group , Ohmstraße 2, 84030 Landshut , Germany

3. Christoph Kracke

   BMW Group (Germany)

   Affiliation as printed

   BMW Group , Ohmstraße 2, 84030 Landshut , Germany

4. Bas van Stein Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science , Niels Bohrweg 1, 2333 CA Leiden , The Netherlands

5. Thomas Bäck Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science , Niels Bohrweg 1, 2333 CA Leiden , The Netherlands