@inproceedings{VoigtMoeckel2021, author = {Voigt, Jorrit and M{\"o}ckel, Michael}, title = {Comparing principal component analysis (PCA) and 𝛃-variational autoencoder(𝛃-VAE)for anomaly detection in selective laser melting (SLM) process data}, series = {14th WCCM-ECCOMAS Congress 2020, virtual congress, January, 11-15, 2021}, booktitle = {14th WCCM-ECCOMAS Congress 2020, virtual congress, January, 11-15, 2021}, editor = {Chinesta, R. and Abgrall, R. and Allix, O. and Kaliske, M.}, doi = {10.23967/wccm-eccomas.2020.144}, pages = {1 -- 9}, year = {2021}, abstract = {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.}, subject = {Selektives Laserschmelzen}, language = {en} }