tool-condition-monitoring

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ml-tool-wear
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"
deep-learning masc-thesis machinery-condition-monitoring tool-wear-monitoring anomaly-detection beta-vae tool-condition-monitoring advanced-manufacturing latent-spaces milling recall trend precision
Language:Jupyter Notebook 54

tvhahn / ml-tool-wear