IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i5d10.1007_s10845-019-01509-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-019-01509-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-019-01509-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Congying Deng & Jielin Tang & Jianguo Miao & Yang Zhao & Xiang Chen & Sheng Lu, 2023. "Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2263-2279, June.
    2. Minglong Guo & Zhaocheng Wei & Minjie Wang & Shiquan Li & Jia Wang & Shengxian Liu, 2021. "Modal parameter identification of general cutter based on milling stability theory," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 221-235, January.
    3. Feng Feng & Meng Yuan & Yousheng Xia & Haoming Xu & Pingfa Feng & Xinghui Li, 2022. "Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    4. Yu Wang & Mingkai Zhang & Xiaowei Tang & Fangyu Peng & Rong Yan, 2022. "A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1483-1502, June.
    5. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01509-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.