Chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT

  • Jian Gao
  • Qinghua Song
  • Zhanqiang Liu


Milling chatter is one of the biggest obstacles to achieve high performance machining operations of thin-walled workpiece in industry field. In the milling process, the time-varying and position-dependent characteristics of thin-walled components are evident. So, effective identification of modal parameters and chatter monitoring are crucial. Although the advantage of chatter monitoring by sound signals is obvious, the milling sound signals are nonstationary signals which contain more stability information both in time domain and frequency domain, and the common analytical transformation methods are no longer applicable. In this paper, short time Fourier transform (STFT) is taken as an example to compare the processing results with cmor continuous wavelet transform (CMWT). This article concerns the chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT. CMWT combines the advantages of the cmor wavelet and continuous wavelet transform which has good locality and the optimal time-frequency resolution. Therefore, CMWT can be adaptively adjusted signal by the window, which is very suitable for processing nonstationary milling signals. Firstly, the model and characteristics of thin-walled workpiece during the cutting process are presented. Secondly, the CMWT method for chatter detection based on acoustic signals in thin-walled component milling process is presented. And the chatter detection results and stability region acquisitions are analyzed and discussed through a specific thin-walled part milling process. Finally, the accuracy of the method presented is verified through the traditional stability lobe diagram predicted using the exiting numerical method and the machined surface morphologies at different cutting positions obtained through the confocal laser microscope.


Chatter detection Thin-walled components Cmor continuous wavelet transform Acoustic signal 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors are grateful to the financial supports of the National Natural Science Foundation of China (no. 51575319), Young Scholars Program of Shandong University (no. 2015WLJH31), the United Fund of Ministry of Education for Equipment Pre-research (no. 6141A02022116), and the Key Research and Development Plan of Shandong Province (no. 2018GGX103007).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Song QH, Ai X, Tang WX (2011) Prediction of simultaneous dynamic stability limit of time-variable parameters system in thin-walled workpiece high-speed milling processes. Int J Adv Manuf Technol 55(9–12):883–889CrossRefGoogle Scholar
  2. 2.
    Luo M, Luo H, Zhang D, Tang K (2018) Improving tool life in multi-axis milling of Ni-based superalloy with ball-end cutter based on the active cutting edge shift strategy. J Mater Process Tech 252:105–115CrossRefGoogle Scholar
  3. 3.
    Ryabov O, Mori K, Kasashima N (1998) Laser displacement meter application for milling diagnostics. Opt Lasers Eng 30(3–4):251–263CrossRefGoogle Scholar
  4. 4.
    Devillez A, Dudzinski D (2007) Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers. Mech Syst Signal Process 21(1):441–456CrossRefGoogle Scholar
  5. 5.
    Pérez-Canales D, Vela-Martínez L, Carlos Jáuregui-Correa J, Alvarez-Ramirez J (2012) Analysis of the entropy randomness index for machining chatter detection. Int J Mach Tools Manuf 62(1):39–45CrossRefGoogle Scholar
  6. 6.
    Lamraoui M, Thomas M, El Badaoui M (2014) Cyclostationarity approach for monitoring chatter and tool wear in high speed milling. Mech Syst Signal Process 44(1–2):177–198CrossRefGoogle Scholar
  7. 7.
    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):2075–2080CrossRefGoogle Scholar
  8. 8.
    Cao HR, Zhou K, Chen XF (2015) Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators. Int J Mach Tools Manuf 92:52–59CrossRefGoogle Scholar
  9. 9.
    Gabriel RF, Alexandru E, Ionuţ CC (2012) Method for early detection of the regenerative instability in turning. Int J Adv Manuf Technol 58(1–4):29–43Google Scholar
  10. 10.
    Huang P, Li J, Sun J, Zhou J (2013) Vibration analysis in milling titanium alloy based on signal processing of cutting force. Int J Adv Manuf Technol 64(5–8):613–621CrossRefGoogle Scholar
  11. 11.
    Luo M, Luo H, Axinte D, Liu DS, Mei JW, Liao ZR (2018) A wireless instrumented milling cutter system with embedded PVDF sensors. Mech Syst Signal Process 110:556–568CrossRefGoogle Scholar
  12. 12.
    Tangjitsitcharoen S, Saksri T, Ratanakuakangwan S (2015) Advance in chatter detection in ball end milling process by utilizing wavelet transform. J Intell Manuf 26(3):1–15CrossRefGoogle Scholar
  13. 13.
    Liu Y, Wu B, Ma J, Zhang D (2016) Chatter identification of the milling process considering dynamics of the thin-walled workpiece. Int J Adv Manuf Technol 2016:1–9Google Scholar
  14. 14.
    Tsai NC, Chen DC, Lee RM (2010) Chatter prevention for milling process by acoustic signal feedback. Int J Adv Manuf Technol 47(9–12):1013–1021CrossRefGoogle Scholar
  15. 15.
    Nair U, Krishna BM, Namboothiri VNN, Nampoori VPN (2010) Permutation entropy based real-time chatter detection using audio signal in turning process. Int J Adv Manuf Technol 46(1–4):61–68CrossRefGoogle Scholar
  16. 16.
    Hynynen KM, Ratava J, Lindh T, Rikkonen M, Ryynänen V, Lohtander M, Varis J (2014) Chatter detection in turning processes using coherence of acceleration and audio signals. J Manuf Sci Eng 136(4):044503CrossRefGoogle Scholar
  17. 17.
    Thaler T, Potočnik P, Bric I, Govekar E (2014) Chatter detection in band sawing based on discriminant analysis of sound features. Appl Acoust 77(77):114–121CrossRefGoogle Scholar
  18. 18.
    Marinescu I, Axinte DA (2008) A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. Int J Mach Tools Manuf 48(10):1148–1160CrossRefGoogle Scholar
  19. 19.
    Delio T, Tlusty J, Smith S (2008) Use of audio signals for chatter detection and control. J Manuf Sci Eng 114(2):146CrossRefGoogle Scholar
  20. 20.
    Li X, Guan XP (2004) Time-frequency-analysis-based minor cutting edge fracture detection during end milling. Mech Syst Signal Process 18(6):1485–1496MathSciNetCrossRefGoogle Scholar
  21. 21.
    Liu HQ, Chen QH, Li B, Mao XY, Mao KM, Peng FY (2011) On-line chatter detection using servo motor current signal in turning. Sci China Technol Sci 54(12):3119–3129CrossRefzbMATHGoogle Scholar
  22. 22.
    Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4–5):672–693CrossRefGoogle Scholar
  23. 23.
    Tangjitsitcharoen S, Pongsathornwiwat N (2013) Development of chatter detection in milling processes. Int J Adv Manuf Technol 65(5–8):919–927CrossRefGoogle Scholar
  24. 24.
    Song QH, Liu ZQ, Wan Y, Ju GG, Shi JH (2015) Application of Sherman-Morrison-Woodbury formulas in instantaneous dynamic of peripheral milling for thin-walled component. Int J Mech Sci 96-97:79–90CrossRefGoogle Scholar
  25. 25.
    Liu C, Zhu L, Ni C (2017) The chatter identification in end milling based on combining EMD and WPD. Int J Adv Manuf Technol 91(9–12):1–10Google Scholar
  26. 26.
    Sheng Q, Zhao J, Wang T (2016) Three-dimensional stability prediction and chatter analysis in milling of thin-walled plate. Int J Adv Manuf Technol 86(5–8):2291–2300Google Scholar
  27. 27.
    Fang N, Pai PS, Edwards N (2014) A method of using Hoelder exponents to monitor tool-edge wear in high-speed finish machining. Int J Adv Manuf Technol 72(9–12):1593–1601CrossRefGoogle Scholar
  28. 28.
    Mallat SG (2009) A wavelet tour of signal processing: the sparse. way 31(3):83–85zbMATHGoogle Scholar
  29. 29.
    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):713–719CrossRefGoogle Scholar
  30. 30.
    Cao HR, Lei YG, He ZG (2013) Chatter identification in end milling process using wavelet packets and Hilbert-Huang transform. Int J Mach Tools Manuf 69(3):11–19CrossRefGoogle Scholar
  31. 31.
    Jiang AY, Zhang C (2006) Hybrid HMM/SVM method for predicting cutting chatter. Proc SPIE Int Soc Opt Eng 62801:8Google Scholar
  32. 32.
    Chen GS, Zheng QZ (2017) Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination. Int J Adv Manuf Technol 5:1–10Google Scholar
  33. 33.
    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 108:106–112CrossRefGoogle Scholar
  34. 34.
    Sun Y, Zhuang C, Xiong Z (2014) Real-time chatter detection using the weighted wavelet packet entropy. Int Conf Adv Intel Mech 2014:1652–1657Google Scholar
  35. 35.
    Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Robot Comput Integr Manuf 25(6):989–998CrossRefGoogle Scholar
  36. 36.
    Shi JH, Song QH, Liu ZQ, Ai X (2017) A novel stability prediction approach for thin-walled component milling considering material removing process. Chin J Aeronaut 30(5):1789–1798CrossRefGoogle Scholar
  37. 37.
    Song QH, Shi JH, Liu ZQ, Wan Y (2016) A time-space discretization method in milling stability prediction of thin-walled component. Int J Adv Manuf Technol 2016:1–15Google Scholar
  38. 38.
    Cao H, Yue Y, Chen X, Zhang X (2016) Chatter detection in milling process based on synchrosqueezing transform of sound signals. Int J Adv Manuf Technol 2016:1–9Google Scholar
  39. 39.
    Lin J, Qu LS (2000) Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J Sound Vib 234(1):135–148CrossRefGoogle Scholar
  40. 40.
    Yi H, Shu H (2012) The improvement of the Morlet wavelet for multi-period analysis of climate data. Compt Rendus Géosci 344(10):483–497CrossRefGoogle Scholar
  41. 41.
    Ao Y, Qiao G (2010) Prognostics for drilling process with wavelet packet decomposition. Int J Adv Manuf Technol 50(1–4):47–52CrossRefGoogle Scholar
  42. 42.
    Prakash M, Kanthababu M, Rajurkar KP (2015) Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy. Int J Adv Manuf Technol 77(5–8):1499–1511CrossRefGoogle Scholar
  43. 43.
    Seemuang N, Mcleay T, Slatter T (2016) Using spindle noise to monitor tool wear in a turning process. Int J Adv Manuf Technol 86(9–12):2781–2790CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China
  2. 2.National Demonstration Center for Experimental Mechanical Engineering EducationShandong UniversityJinanPeople’s Republic of China

Personalised recommendations