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Ran an die Fasern
(2019)
Neues Recyclingverfahren
(2019)
This study demonstrates mobile load monitoring of a composite bicycle component using an application-
customized polymer planar Bragg grating sensor, evaluated by a mobile interrogation unit. After a referencing procedure, the mechanical load of a seat post is monitored while cycling through a test track.
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.