Comparing principal component analysis (PCA) and β variational autoencoder (β-vae) for anomaly detec tion in selective laser melting (SLM) process data

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 β-variational autoencoder (β-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 β-VAE enables a slightly more compact dimensionality reduction; we consider it an option for automated process monitoring systems.