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Real-time chatter detection and automatic suppression for intelligent spindles based on wavelet packet energy entropy and local outlier factor algorithm

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Abstract

In this research, we proposed a real-time chatter detection and suppression module for intelligent spindle to increase machining efficiency and processing yield. For early detections of chatters, the relative wavelet packet energy entropy with high sensitivity in the high-frequency band and the local outlier factor (LOF) algorithm were utilized as chatter features and classifications, respectively. Based on the pre-obtained three-dimensional stability lobe diagram (SLD) and a LOF-based trained model, the module could real-time monitor and suppress chatter during machining processes. The module was implemented and experimentally examined with the CNC end-milling machine under five different cutting conditions for verifying the capabilities of real-time chatter identifications and suppressions. It was demonstrated that the module could detect the onset of chatter and suppress it by changing the cutting conditions to avoid damages on the surfaces of working piece due to severe chatters.

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Acknowledgements

The authors would like to thank Industrial Technology Research Institute for giving accesses to their facilities and helpful assistances for chatter experiments.

Funding

This work was supported by Ministry of Science and Technology, Taiwan with grant number MOST 106-2221-E-002-133-MY2.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan

    Yung-Chen Yao, Yu-Hsuan Chen, Chien-Hao Liu & Wen-Pin Shih

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  1. Yung-Chen Yao
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  2. Yu-Hsuan Chen
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  3. Chien-Hao Liu
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  4. Wen-Pin Shih
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Correspondence to Chien-Hao Liu.

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Yao, YC., Chen, YH., Liu, CH. et al. Real-time chatter detection and automatic suppression for intelligent spindles based on wavelet packet energy entropy and local outlier factor algorithm . Int J Adv Manuf Technol 103, 297–309 (2019). https://doi.org/10.1007/s00170-019-03551-2

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  • DOI: https://doi.org/10.1007/s00170-019-03551-2

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