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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This work is supported by the National Science Foundation of China (51575423,11772244) and China Scholarship Council (201906280415).
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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
- Chatter detection
- High-speed milling