GitHub topics: tool-wear-monitoring
tvhahn/ml-tool-wear
Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"
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claudiotancredi/Tool-wear-classification
"Machine learning in applications" project @ Politecnico di Torino, a.y. 2021/2022.
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