Chatter detection in milling based on singular spectrum analysis

  • Yonggang Mei
  • Rong Mo
  • Huibin Sun
  • Kun Bu


Chatter is a frequently encountered problem in metal cutting field which reduces the machining efficiency and surface quality. Therefore, a reliable and robust chatter detection method is necessary to improve the machining performances. In this work, a novel milling chatter detection approach based on singular spectrum analysis (SSA) is proposed. SSA is applied to process the cutting force signal and extract the feature that is closely related to the machining state. The singular value spectrum obtained by SSA is used to describe the energy distribution of the principal modes in the signal. On the basis of frequency domain chatter theory, singular value entropy (SVE) is adopted to evaluate the variation of energy distribution in the signal and the milling chatter is detected accordingly. Milling experiments under different cutting conditions are performed out to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can accurately identify the onset of chatter. This method is simple in operation and fast in calculation, which makes it have great potential for online chatter detection.


Chatter detection Milling Singular spectrum analysis Singular value entropy Principal modes 


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  1. 1.
    Arnold R (1946) Cutting tools research: report of subcommittee on carbide tools: the mechanism of tool vibration in the cutting of steel. Proc Instit Mech Eng 154(1):261–284. CrossRefGoogle Scholar
  2. 2.
    Andrew C, Tobias S (1961) A critical comparison of two current theories of machine tool chatter. International Journal of Machine Tool Design and Research 1(4):325–335. CrossRefGoogle Scholar
  3. 3.
    Hanna N, Tobias S (1974) A theory of nonlinear regenerative chatter. ASME J Eng Ind 96(1):247–255. CrossRefGoogle Scholar
  4. 4.
    Minis I, Yanushevsky R, Tembo A, Hocken R (1990) Analysis of linear and nonlinear chatter in milling. CIRP Ann-Manuf Technol 39(1):459–462. CrossRefGoogle Scholar
  5. 5.
    Wiercigroch M, Krivtsov AM (2001) Frictional chatter in orthogonal metal cutting. Philos Trans Royal Soc London A: Math Phys Eng Sci 359(1781):713–738. CrossRefzbMATHGoogle Scholar
  6. 6.
    Rusinek R, Wiercigroch M, Wahi P (2014) Modelling of frictional chatter in metal cutting. Int J Mech Sci 89:167–176. CrossRefGoogle Scholar
  7. 7.
    Tlusty J, Zaton W, Ismail F (1983) Stability lobes in milling. CIRP Ann-Manuf Technol 32(1):309–313. CrossRefGoogle Scholar
  8. 8.
    Yanushevsky B (1993) A new theoretical approach for the prediction of machine tool chatter in milling. J Eng Ind 115:1CrossRefGoogle Scholar
  9. 9.
    Altintas Y, Shamoto E, Lee P, Budak E (1999) Analytical prediction of stability lobes in ball end milling. J Manuf Sci Eng 121(4):586–592. CrossRefGoogle Scholar
  10. 10.
    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. CrossRefGoogle Scholar
  11. 11.
    Pour M, Torabizadeh M (2016) Improved prediction of stability lobes in milling process using time series analysis. J Intell Manuf 27(3):665–677. CrossRefGoogle Scholar
  12. 12.
    Delio T, Tlusty J, Smith S (1992) Use of audio signals for chatter detection and control. ASME J Eng Ind 114(2):146–157Google Scholar
  13. 13.
    Tansel I, Li M, Demetgul M, Bickraj K, Kaya B, Ozcelik B (2012) Detecting chatter and estimating wear from the torque of end milling signals by using index based reasoner (IBR). Int J Adv Manuf Technol 58(1):109–118. CrossRefGoogle Scholar
  14. 14.
    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):1045–1056Google Scholar
  15. 15.
    Mei C (2005) Active regenerative chatter suppression during boring manufacturing process. Robot Comput Integr Manuf 21(2):153–158. CrossRefGoogle Scholar
  16. 16.
    Sims ND (2007) Vibration absorbers for chatter suppression: a new analytical tuning methodology. J Sound Vib 301(3):592–607. CrossRefGoogle Scholar
  17. 17.
    Kakinuma Y, Enomoto K, Hirano T, Ohnishi K (2014) Active chatter suppression in turning by band-limited force control. CIRP Ann-Manuf Technol 63(1):365–368. CrossRefGoogle Scholar
  18. 18.
    Meng H-F, Kang Y, Chen Z, Zhao Y-B, Liu G-P (2015) Stability analysis and stabilization of a class of cutting systems with chatter suppression. IEEE/ASME Trans Mech 20(2):991–996. CrossRefGoogle Scholar
  19. 19.
    Munoa J, Beudaert X, Dombovari Z, Altintas Y, Budak E, Brecher C, Stepan G (2016) Chatter suppression techniques in metal cutting. CIRP Ann-Manuf Technol 65(2):785–808. CrossRefGoogle Scholar
  20. 20.
    Karpuschewski B, Wehmeier M, Inasaki I (2000) Grinding monitoring system based on power and acoustic emission sensors. CIRP Annals-Manufacturing Technology 49(1):235–240. CrossRefGoogle Scholar
  21. 21.
    Yoon M, Chin D (2005) Cutting force monitoring in the endmilling operation for chatter detection. Proc Inst Mech Eng B J Eng Manuf 219(6):455–465. CrossRefGoogle Scholar
  22. 22.
    Choi T, Shin YC (2003) On-line chatter detection using wavelet-based parameter estimation. Trans-Am Society Mech Eng J Manuf Sci Eng 125(1):21–28. Google Scholar
  23. 23.
    Du R, Elbestawi M, Ullagaddi B (1992) Chatter detection in milling based on the probability distribution of cutting force signal. Mech Syst Signal Process 6(4):345–362. CrossRefGoogle Scholar
  24. 24.
    Shao Y, Deng X, Yuan Y, Mechefske CK, Chen Z (2014) Characteristic recognition of chatter mark vibration in a rolling mill based on the non-dimensional parameters of the vibration signal. J Mech Sci Technol 28(6):2075–2080. CrossRefGoogle Scholar
  25. 25.
    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. CrossRefGoogle Scholar
  26. 26.
    Gradišek J, Baus A, Govekar E, Klocke F, Grabec I (2003) Automatic chatter detection in grinding. Int J Mach Tools Manuf 43(14):1397–1403. CrossRefGoogle Scholar
  27. 27.
    Nair U, Krishna BM, Namboothiri V, Nampoori V (2010) Permutation entropy based real-time chatter detection using audio signal in turning process. Int J Adv Manuf Technol 46(1–4):61–68. CrossRefGoogle Scholar
  28. 28.
    Zhang Z, Li H, Meng G, Tu X, Cheng C (2016) Chatter detection in milling process based on the energy entropy of VMD and WPD. Int J Mach Tools Manuf 108:106–112. CrossRefGoogle Scholar
  29. 29.
    Golyandina N, Zhigljavsky A (2013) Singular spectrum analysis for time series. Springer Science & Business Media, Heidelberg
  30. 30.
    Hassani H, Heravi S, Zhigljavsky A (2009) Forecasting European industrial production with singular spectrum analysis. Int J Forecast 25(1):103–118. CrossRefGoogle Scholar
  31. 31.
    Colebrook J (1978) Continuous plankton records-zooplankton and environment, northeast Atlantic and North-Sea, 1948-1975. Oceanol Acta 1(1):9–23Google Scholar
  32. 32.
    Vautard R, Ghil M (1989) Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Physica D: Nonlinear Phenomena 35(3):395–424. MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Wang W, Chen J, Wu X, Wu Z (2001) The application of some non-linear methods in rotating machinery fault diagnosis. Mech Syst Signal Process 15(4):697–705. CrossRefGoogle Scholar
  34. 34.
    Alonso F, Salgado D (2005) Application of singular spectrum analysis to tool wear detection using sound signals. Proc Inst Mech Eng B J Eng Manuf 219(9):703–710. CrossRefGoogle Scholar
  35. 35.
    Lu C-J, Tsai D-M (2005) Automatic defect inspection for LCDs using singular value decomposition. Int J Adv Manuf Technol 25(1):53–61. CrossRefGoogle Scholar
  36. 36.
    Salgado D, Alonso F (2006) Tool wear detection in turning operations using singular spectrum analysis. J Mater Process Technol 171(3):451–458. CrossRefGoogle Scholar
  37. 37.
    Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge university press, UKGoogle Scholar
  38. 38.
    Golyandina N (2002) Analysis of time series structure: SSA and related techniques. Chapman & Hall/CRC, Boca Raton, FLGoogle Scholar
  39. 39.
    Elsner JB, Tsonis AA (1996) Singular spectrum analysis: a new tool in time series analysis. Springer Science & Business Media, BerlinGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China

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