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A data-centric approach to anomaly detection in layer-based additive manufacturing. (arXiv:2209.10178v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 …
additive manufacturing anomaly anomaly detection arxiv data data-centric detection manufacturing