The usability of machine learning approaches for the development of in-situ process monitoring, automated anomaly detection and quality assurance for the selective laser melting (SLM) process receives currently increasing attention. For a given set of real machine data we compare two established methods, principal component analysis (PCA) and -variational autoencoder (ß-VAE), for their applicability in exploratory data analysis and anomaly detection. We introduce a PCA-based unsupervised feature extraction algorithm, which allows for root cause analysis of process anomalies. The ß-VAE enables a slightly more compact dimensionality reduction; we consider it an option for automated process monitoring systems.

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Published on 10/03/21
Submitted on 10/03/21

Volume 1000 - Manufacturing and Materials Processing, 2021
DOI: 10.23967/wccm-eccomas.2020.144
Licence: CC BY-NC-SA license

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