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Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method

Author

Listed:
  • Jianfeng Tao

    (Shanghai Jiao Tong University)

  • Chengjin Qin

    (Shanghai Jiao Tong University)

  • Dengyu Xiao

    (Shanghai Jiao Tong University)

  • Haotian Shi

    (Shanghai Jiao Tong University)

  • Xiao Ling

    (Shanghai Jiao Tong University)

  • Bingchu Li

    (University of Shanghai for Science and Technology)

  • Chengliang Liu

    (Shanghai Jiao Tong University)

Abstract

Induced by flexibility of the industrial robot, cutting tool or the workpiece, chatter in robotic machining process has detrimental effects on the surface quality, tool life and machining productivity. Consequently, accurate detection and timely suppression for such undesirable vibration is desperately needed to achieve high performance robotic machining. This paper presents a novel approach combining the notch filter and local maximum synchrosqueezing transform for the timely chatter identification in robotic drilling. The proposed approach is accomplished through the following steps. In the first step, the optimal matrix notch filter is designed to eliminate the interference of the spindle frequency and corresponding harmonic components to the measured acceleration signal. Subsequently, the high-resolution time–frequency information of the non-stationary filtered acceleration signal is acquired by employing local maximum synchrosqueezing transform (LMSST). On this basis, the filtered acceleration signal is divided into a finite number of equal-width frequency bands, and the corresponding sub-signal for each frequency band is obtained by summing the corresponding coefficient of the LMSST. Finally, to accurately depict the non-uniformity of energy distribution during the chatter incubation process, the statistical energy entropy is calculated and utilized as the indicator to detect chatter online. The effectiveness of the proposed approach is validated by a large number of robot drilling experiments with different cutting tools, workpiece materials and machining parameters. The results show that the presented local maximum synchrosqueezing-based approach can effectively recognize the chatter at an early stage during its incubation and development process.

Suggested Citation

  • Jianfeng Tao & Chengjin Qin & Dengyu Xiao & Haotian Shi & Xiao Ling & Bingchu Li & Chengliang Liu, 2020. "Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1243-1255, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01509-5
    DOI: 10.1007/s10845-019-01509-5
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    References listed on IDEAS

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    1. M. Pour & M. A. Torabizadeh, 2016. "Improved prediction of stability lobes in milling process using time series analysis," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 665-677, June.
    2. Yang Fu & Yun Zhang & Huang Gao & Ting Mao & Huamin Zhou & Ronglei Sun & Dequn Li, 2019. "Automatic feature constructing from vibration signals for machining state monitoring," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 995-1008, March.
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    Cited by:

    1. Antonio Caputi & Davide Russo, 2021. "The optimization of the control logic of a redundant six axis milling machine," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1441-1453, June.
    2. Guo Zhou & Kai Zhou & Jing Zhang & Meng Yuan & Xiaohao Wang & Pingfa Feng & Min Zhang & Feng Feng, 2024. "Digital modeling-driven chatter suppression for thin-walled part manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 289-305, January.
    3. Zeqing Yang & Mingxuan Zhang & Yingshu Chen & Ning Hu & Lingxiao Gao & Libing Liu & Enxu Ping & Jung Il Song, 2024. "Surface defect detection method for air rudder based on positive samples," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 95-113, January.

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