  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. Additionally, 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.
