Experimental Techniques

, Volume 42, Issue 2, pp 141–153 | Cite as

Analysis of Tool Chatter in Terms of Chatter Index and Severity Using a New Adaptive Signal Processing Technique

  • Y. Shrivastava
  • B. Singh
  • A. Sharma


Regenerative chatter is a predominant phenomenon in the turning process. In machining of metals, identification of tool chatter is essential in order to improve productivity and thus enhance the tool life. In spite of the immense work done within this domain, still many facets are yet to be explored. For detection of chatter many researchers have used sensors, but usually measured chatter signals from sensors are contaminated by background noise and other disturbances. Hence, it is essential to develop an efficient signal processing technique by the aid of which induced noise can be removed and onset of chatter can be detected at the earliest. In present work the scholar has explored four objectives. Firstly, turning process has been simulated using simulink tool Matlab and signals have been recorded at different combinations of cutting parameters. The simulation has been validated by comparing the simulated and experimentally recorded signals. Secondly, wavelet denoising technique has been implemented for denoising the noisy signals. Moreover, the denoising technique have been validated by comparing the results with simulated noise free signals. Thirdly, the peak frequency have been determined at different combination of cutting parameter by power spectral density analysis. Lastly, a new output parameter chatter index (CI) has been calculated. CI makes it convenient to analysis the severity of chatter.


Wavelet Denoising Chatter Simulink Chatter index Chatter severity 


  1. 1.
    Deshpande N, Fofana M (2001) Nonlinear regenerative chatter in turning. Robot Comput Integr Manuf 17(1):107–112CrossRefGoogle Scholar
  2. 2.
    Stépán G, Insperger T, Szalai R (2005) Delay, parametric excitation, and the nonlinear dynamics of cutting processes. Int J Bifurcation Chaos 15(09):2783–2798CrossRefGoogle Scholar
  3. 3.
    Suh C, Khurjekar P, Yang B (2002) Characterisation and identification of dynamic instability in milling operation. Mech Syst Signal Process 16(5):853–872CrossRefGoogle Scholar
  4. 4.
    Faassen R, Van de Wouw N, Oosterling J, Nijmeijer H (2003) Prediction of regenerative chatter by modelling and analysis of high-speed milling. Int J Mach Tools Manuf 43(14):1437–1446CrossRefGoogle Scholar
  5. 5.
    Toh C (2004) Vibration analysis in high speed rough and finish milling hardened steel. J Sound Vib 278(1):101–115CrossRefGoogle Scholar
  6. 6.
    Li X (2002) A brief review: acoustic emission method for tool wear monitoring during turning. Int J Mach Tools Manuf 42(2):157–165CrossRefGoogle Scholar
  7. 7.
    Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4):672–693CrossRefGoogle Scholar
  8. 8.
    Kuljanic E, Totis G, Sortino M (2009) Development of an intelligent multisensor chatter detection system in milling. Mech Syst Signal Process 23(5):1704–1718CrossRefGoogle Scholar
  9. 9.
    Ismail F, Ziaei R (2002) Chatter suppression in five-axis machining of flexible parts. Int J Mach Tools Manuf 42(1):115–122CrossRefGoogle Scholar
  10. 10.
    Schmitz TL (2003) Chatter recognition by a statistical evaluation of the synchronously sampled audio signal. J Sound Vib 262(3):721–730CrossRefGoogle Scholar
  11. 11.
    Berger B, Minis I, Harley J, Rokni M, Papadopoulos M (1998) Wavelet based cutting state identification. J Sound Vib 213(5):813–827CrossRefGoogle Scholar
  12. 12.
    Khraisheh M, Pezeshki C, Bayoumi A (1995) Time series based analysis for primary chatter in metal cutting. J Sound Vib 180(1):67–87CrossRefGoogle Scholar
  13. 13.
    Shamoto E, Fujimaki S, Sencer B, Suzuki N, Kato T, Hino R (2012) A novel tool path/posture optimization concept to avoid chatter vibration in machining–Proposed concept and its verification in turning. CIRP Ann Manuf Technol 61(1):331–334CrossRefGoogle Scholar
  14. 14.
    Tobias S, Fishwick W (1958) Theory of regenerative machine tool chatter. Eng 205(7):199–203Google Scholar
  15. 15.
    Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 61:27–47CrossRefGoogle Scholar
  16. 16.
    Version, M.: 7.9. 0.529 (2009b). The Language of Technical Computing (2009)Google 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 69:11–19CrossRefGoogle Scholar
  18. 18.
    Peng Z, Chu F (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221CrossRefGoogle Scholar
  19. 19.
    Lin J, Zuo M (2003) Gearbox fault diagnosis using adaptive wavelet filter. Mech Syst Signal Process 17(6):1259–1269CrossRefGoogle Scholar
  20. 20.
    Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process 9(9):1532–1546CrossRefGoogle Scholar
  21. 21.
    Stoica P, Moses RL (1997) Introduction to spectral analysis, vol 1. Prentice hall Upper Saddle River, NJGoogle Scholar
  22. 22.
    Liu C, Zhu L, Ni C (2017) The chatter identification in end milling based on combining EMD and WPD. Int J Adv Manuf Technol:1–10.
  23. 23.
    Goldfischer LI (1965) Autocorrelation function and power spectral density of laser-produced speckle patterns. J Opt Soc Am 55(3):247–253CrossRefGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc 2017

Authors and Affiliations

  1. 1.Manufacturing Laboratory, MEDJaypee University of Engineering and TechnologyGunaIndia

Personalised recommendations