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Predicting chatter using machine learning and acoustic signals from low-cost microphones

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Abstract

Machining chatter is a phenomenon resulting from self-oscillation between a machining tool and workpiece. This self-oscillation results in variation on the machined product that reduces the ability to meet desired specifications. Chatter is a widely studied topic as it directly relates to the quality of machined products. This study details the application of a Random Forest (RF) classifier with Recursive Feature Elimination (RFE) to machining audio collected by a single microphone during down-milling operations. This approach allows straightforward feature elimination that results in an easily understood set of analyzed dimensions. Stability is predicted solely based on the classification output of the RF classifier. Our approach proves highly predictive with consistent machining setup and a small sample set. We also review transferability between machining setups and present key findings. Our RF approach demonstrates the ability to analyze and classify chatter through a low-cost approach with limited training data required. The motivation for using a single microphone is to enable detection on machines without other sensors, such as accelerometers, present in the machining setup. The value of the in-process sensor and chatter classifier is highlighted because the machining setup included asymmetric dynamics that reduced the accuracy of the traditional analytical stability solution. We see a natural progression to deploying this audio-only methodology with real-time processing and classification using either a laptop or smartphone. This progression will allow visual indicators during the machining process that can alert machinists of progression into unstable machining processes.

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Data availability

Audio files and derived data are not publicly available.

Code availability

Code written in this study is not publicly available.

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Funding

The research leading to these results received funding in part from UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

The authors also gratefully acknowledge seed funding from the University of Tennessee-Oak Ridge Innovation Institute (UT-ORII) to partially support this research.

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Tennessee, 851 Neyland Drive, Knoxville, 37996, TN, USA

    Sam St. John, Matthew Alberts & Anahita Khojandi

  2. Manufacturing Science Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, 37830, TN, USA

    Jaydeep Karandikar & Tony Schmitz

  3. Department of Nuclear Engineering, University of Tennessee, 863 Neyland Drive, Knoxville, 37996, TN, USA

    Jamie Coble

  4. Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, 1512 Middle Drive, Knoxville, 37996, TN, USA

    Bradley Jared & Tony Schmitz

  5. Institute of Production Engineering and Photonic Technologies, TU Wien, Vienna, Austria

    Christoph Ramsauer & David Leitner

Authors
  1. Sam St. John
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Contributions

Conceptualization, creation of data pipeline, and experimentation within data pipeline done by SS. Article writing done by SS with key contributions for machining from JK, BJ, and TS. Data acquisition for machining audio performed by JK, TS, CR, and DL. Review of paper done by MA, JK, JC, BJ, CR, DL, and AK.

Corresponding author

Correspondence to Anahita Khojandi.

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This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Appendices

Appendix A. Audio file machining parameters

Table 8 Audio file machining parameters from all data sets
Full size table

Appendix B. Key feature abbreviations

Table 9 Key spectral feature abbreviations
Full size table
Table 10 Key harmonics feature abbreviations
Full size table
Table 11 Key peak feature abbreviations
Full size table

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St. John, S., Alberts, M., Karandikar, J. et al. Predicting chatter using machine learning and acoustic signals from low-cost microphones. Int J Adv Manuf Technol 125, 5503–5518 (2023). https://doi.org/10.1007/s00170-023-10918-z

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