About: Abstract Acoustic monitoring presents itself as a flexible but under-reported method of tool condition monitoring in milling operations. This paper demonstrates the power of the monitoring paradigm by presenting a method of characterizing milling tool conditions by detecting anomalies in the time-frequency domain of the tools’ acoustic spectrum during cutting operations. This is done by training a generative adversarial neural network on only a single, readily obtained class of acoustic data and then inverting the generator to perform anomaly detection. Anomalous and non-anomalous data are shown to be nearly linearly separable using the proposed method, resulting in 90.56% tool condition classification accuracy and a 24.49% improvement over classification without the method.   Goto Sponge  NotDistinct  Permalink

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  • Abstract Acoustic monitoring presents itself as a flexible but under-reported method of tool condition monitoring in milling operations. This paper demonstrates the power of the monitoring paradigm by presenting a method of characterizing milling tool conditions by detecting anomalies in the time-frequency domain of the tools’ acoustic spectrum during cutting operations. This is done by training a generative adversarial neural network on only a single, readily obtained class of acoustic data and then inverting the generator to perform anomaly detection. Anomalous and non-anomalous data are shown to be nearly linearly separable using the proposed method, resulting in 90.56% tool condition classification accuracy and a 24.49% improvement over classification without the method.
Subject
  • Convex analysis
  • Machine learning
  • Geometry
  • Anomalies in physics
  • Mathematical psychology
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