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Chatter detection in high-speed milling processes based on ON-LSTM and PBT

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Abstract

Chatter is a kind of self-excited vibration which frequently occurs in high-speed milling processes, which induces severe damage to both spindle tools and workpieces. In this paper, we introduce a new chatter detection technique using ordered-neurons long short-term memory (ON-LSTM) and population based training (PBT). First, we conduct a large number of milling experiments on a computer numerical control (CNC) milling machine with 4 accelerometers to get the dataset and employ vanilla LSTM for chatter detection. Then, to interpret the performance on time series of recurrent neural networks (RNN), a variation of LSTM named ON-LSTM is applied to chatter detection and a hyperparameter tuning method PBT is used for training. Finally, we compare the trained ON-LSTM with the time-frequency spectrum of the original signals obtained by short-time Fourier transform (STFT), and they show a certain degree of consistency.

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Funding

This work is supported by the National Science Foundation of China (51575423,11772244) and China Scholarship Council (201906280415).

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Authors and Affiliations

  1. Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

    Fei Shi & Hongrui Cao

  2. Computer Science, University of California, Santa Barbara, Santa Barbara, CA, USA

    Yuke Wang, Boyuan Feng & Yufei Ding

Authors
  1. Fei Shi
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  2. Hongrui Cao
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  3. Yuke Wang
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  4. Boyuan Feng
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  5. Yufei Ding
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Correspondence to Hongrui Cao.

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Shi, F., Cao, H., Wang, Y. et al. Chatter detection in high-speed milling processes based on ON-LSTM and PBT. Int J Adv Manuf Technol 111, 3361–3378 (2020). https://doi.org/10.1007/s00170-020-06292-9

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  • DOI: https://doi.org/10.1007/s00170-020-06292-9

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