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Detecting machine chatter using audio data and machine learning

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Abstract

We present a method for detecting chatter in CNC machining. Our method uses machining learning to train a classifier to determine the chatter threshold, and we use an autoencoder to reduce the dimensionality of the data. We test our method on machining audio data, and successfully detect chatter in the validation data. Our method is amenable to use on the shop floor, as a machinist using our method needs only to classify audio as chatter and non-chatter.

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Authors and Affiliations

  1. Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave W, N2L 3G1, Waterloo, Canada

    Ilarion Kvinevskiy & Sanjeev Bedi

  2. Cheriton School of Computer Science, University of Waterloo, 200 University Ave W, N2L 3G1, Waterloo, Canada

    Stephen Mann

Authors
  1. Ilarion Kvinevskiy
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  2. Sanjeev Bedi
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  3. Stephen Mann
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Correspondence to Stephen Mann.

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Kvinevskiy, I., Bedi, S. & Mann, S. Detecting machine chatter using audio data and machine learning. Int J Adv Manuf Technol 108, 3707–3716 (2020). https://doi.org/10.1007/s00170-020-05571-9

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  • DOI: https://doi.org/10.1007/s00170-020-05571-9

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