Comparing Principal Component Analysis (PCA) and -Variational Autoencoder (ß-VAE) for Anomaly Detection in Selective Laser Melting (SLM) Process Data
Year: 2021
Type: conference-paper
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 (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.
Source: WCCM-ECCOMAS Congress
Authors: J. Voigt, M. Moeckel
Institution: Aschaffenburg University of Applied Sciences
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3139521147