TY - CHAP A1 - Voigt, Jorrit A1 - Möckel, Michael ED - Chinesta, R. ED - Abgrall, R. ED - Allix, O. ED - Kaliske, M. T1 - Comparing principal component analysis (PCA) and 𝛃-variational autoencoder(𝛃-VAE)for anomaly detection in selective laser melting (SLM) process data T2 - 14th WCCM-ECCOMAS Congress 2020, virtual congress, January, 11-15, 2021 N2 - 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 (-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 -VAE enables a slightly more compact dimensionality reduction; we consider it an option for automated process monitoring systems. KW - Anomaly detection KW - Process monitoring KW - additive manufacturing KW - Selektives Laserschmelzen KW - Anomalie Y1 - 2021 UR - https://www.scipedia.com/public/Voigt_Moeckel_2021a U6 - https://doi.org/10.23967/wccm-eccomas.2020.144 SP - 1 EP - 9 ER -