Anomaly detection in milling tools using acoustic signals and generative adversarial networks

Clayton Cooper, Jianjing Zhang, Robert X. Gao, Peng Wang, Ihab Ragai

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)372-378
Number of pages7
JournalProcedia Manufacturing
StatePublished - 2020
Event48th SME North American Manufacturing Research Conference, NAMRC 48 - Cincinnati, United States
Duration: Jun 22 2020Jun 26 2020

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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