Chatter detection based on synchrosqueezing transform and statistical indicators in milling process

  • Hongrui Cao
  • Yiting Yue
  • Xuefeng Chen
  • Xingwu Zhang
ORIGINAL ARTICLE
First Online:
Received:
Accepted:
  • 109 Downloads

Abstract

Chatter is a self-excited vibration between the workpiece and tool. In view of the non-stationarity of the chatter signal, the synchrosqueezing transform (SST) is used to process vibration signals during cutting, which can enhance the energy ratio of chatter. In order to eliminate the interference of tooth passing frequency and its harmonics, a time-frequency filtering method is applied to filter these frequency components out. Then, the vibration signal is reconstructed by inverse SST and statistical indexes in time and frequency domains are calculated. The cutting tests are carried out to select statistical indexes which are sensitive to chatter. The effectiveness of the proposed method is verified with cutting tests, and the results show that the chatter can be detected successfully before severe chatter marks are left on the workpiece.

Keywords

Chatter detection Statistical indicators Time-frequency filtering Synchrosqueezing transform 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors would like to acknowledge the support of the National Natural Science Foundation of China (No. 51575423, 51421004), the Natural Science Foundation of Shaanxi (No. 2017JM5120), and the Fundamental Research Funds for the Central University.

References

  1. 1.
    Cao H, Li B, He Z (2012) Chatter stability of milling with speed-varying dynamics of spindles. Int J Mach Tool Manu 52(1):50–58.  https://doi.org/10.1016/j.ijmachtools.2011.09.004 CrossRefGoogle Scholar
  2. 2.
    Wan M, Ma Y-C, Zhang W-H, Yang Y (2015) Study on the construction mechanism of stability lobes in milling process with multiple modes. Int J Adv Manuf Technol 79(1–4):589–603.  https://doi.org/10.1007/s00170-015-6829-4 CrossRefGoogle Scholar
  3. 3.
    Wan M, Feng J, Ma Y-C, Zhang W-H (2017) Identification of milling process damping using operational modal analysis. Int J Mach Tools Manuf 122:120–131.  https://doi.org/10.1016/j.ijmachtools.2017.06.006 CrossRefGoogle Scholar
  4. 4.
    Wan M, Ma Y-C, Feng J, Zhang W-H (2016) Study of static and dynamic ploughing mechanisms by establishing generalized model with static milling forces. Int J Mech Sci 114:120–131.  https://doi.org/10.1016/j.ijmecsci.2016.05.010 CrossRefGoogle Scholar
  5. 5.
    Wan M, Zhang W-H, Dang J-W, Yang Y (2010) A unified stability prediction method for milling process with multiple delays. Int J Mach Tools Manuf 50(1):29–41.  https://doi.org/10.1016/j.ijmachtools.2009.09.009 CrossRefGoogle Scholar
  6. 6.
    Yang Y, Zhang W-H, Ma Y-C, Wan M (2016) Chatter prediction for the peripheral milling of thin-walled workpieces with curved surfaces. Int J Mach Tools Manuf 109:36–48.  https://doi.org/10.1016/j.ijmachtools.2016.07.002 CrossRefGoogle Scholar
  7. 7.
    Cao H, Zhang X, Chen X (2017) The concept and progress of intelligent spindles: a review. Int J Mach Tool Manu 112:21–52.  https://doi.org/10.1016/j.ijmachtools.2016.10.005 CrossRefGoogle Scholar
  8. 8.
    Tangjitsitcharoen S (2009) In-process monitoring and detection of chip formation and chatter for CNC turning. J Mater Process Technol 209(10):4682–4688.  https://doi.org/10.1016/j.jmatprotec.2008.10.054 CrossRefGoogle Scholar
  9. 9.
    Liang M, Yeap T, Hermansyah A (2004) A fuzzy system for chatter suppression in end milling. Proc Inst Mech Eng B J Eng Manuf 218(4):403–417CrossRefGoogle Scholar
  10. 10.
    Thaler T, Potočnik P, Bric I, Govekar E (2014) Chatter detection in band sawing based on discriminant analysis of sound features. Appl Acoust 77:114–121.  https://doi.org/10.1016/j.apacoust.2012.12.004 CrossRefGoogle Scholar
  11. 11.
    Tangjitsitcharoen S, Saksri T, Ratanakuakangwan S (2013) Advance in chatter detection in ball end milling process by utilizing wavelet transform. J Intell Manuf 26(3):485–499.  https://doi.org/10.1007/s10845-013-0805-3 CrossRefGoogle Scholar
  12. 12.
    Shih-Ming W, Ming-Je L, Sheng-Yu L, Hung-Wei L (2007) Application of wavelet transform on diagnosis and prediction of milling chatter. Chinese Journal of Mechanical Engineering 20(3)Google Scholar
  13. 13.
    Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Robot Comput Integr Manuf 25(6):989–998.  https://doi.org/10.1016/j.rcim.2009.04.011 CrossRefGoogle Scholar
  14. 14.
    Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210(5):713–719.  https://doi.org/10.1016/j.jmatprotec.2009.11.007 CrossRefGoogle Scholar
  15. 15.
    Chen B, Yang J, Zhao J, Ren J (2015) Milling Chatter prediction based on the information entropy and support vector machine. In, 2015. Atlantis Press,Google Scholar
  16. 16.
    Chin DH, Yoon MC (2005) Cutting force monitoring in the endmilling operation for chatter detection. Proc Inst Mech Eng B J Eng Manuf 219(6):455–465.  https://doi.org/10.1243/095440505x32292 CrossRefGoogle Scholar
  17. 17.
    Cao H, Lei Y, He Z (2013) Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform. Int J Mach Tools Manuf 69:11–19.  https://doi.org/10.1016/j.ijmachtools.2013.02.007 CrossRefGoogle Scholar
  18. 18.
    Cao H, Zhou K, Chen X, Zhang X (2017) Early chatter detection in end milling based on multi-feature fusion and 3σ criterion. Int J Adv Manuf Technol.  https://doi.org/10.1007/s00170-017-0476-x
  19. 19.
    Liu H, Chen Q, Li B, Mao X, Mao K, Peng F (2011) On-line chatter detection using servo motor current signal in turning. SCIENCE CHINA Technol Sci 54(12):3119–3129.  https://doi.org/10.1007/s11431-011-4595-6 CrossRefMATHGoogle Scholar
  20. 20.
    Fu Y, Zhang Y, Zhou H, Li D, Liu H, Qiao H, Wang X (2016) Timely online chatter detection in end milling process. Mech Syst Signal Process 75:668–688CrossRefGoogle Scholar
  21. 21.
    Cao H, Zhou K, Chen X (2015) Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators. Int J Mach Tools Manuf 92:52–59.  https://doi.org/10.1016/j.ijmachtools.2015.03.002 CrossRefGoogle Scholar
  22. 22.
    Sun H, Zhang X, Wang J (2016) Online machining chatter forecast based on improved local mean decomposition. Int J Adv Manuf Technol 84(5–8):1045–1056.  https://doi.org/10.1007/s00170-015-7785-8 Google Scholar
  23. 23.
    Daubechies I, Lu J, H-T W (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261.  https://doi.org/10.1016/j.acha.2010.08.002 MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Cao H, Yue Y, Chen X, Zhang X (2017) Chatter detection in milling process based on synchrosqueezing transform of sound signals. Int J Adv Manuf Technol 89(9–12):2747–2755.  https://doi.org/10.1007/s00170-016-9660-7 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • Hongrui Cao
    • 1
  • Yiting Yue
    • 1
  • Xuefeng Chen
    • 2
  • Xingwu Zhang
    • 1
  1. 1.Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing SystemXi’an Jiaotong UniversityXi’anChina
  2. 2.State Key Laboratory for Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anChina

Personalised recommendations