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Mirror milling chatter identification using Q-factor and SVM


Mirror milling is an effective approach to improve large-scale monolithic thin-walled parts machining quality through ensuring the mirror relations of cutter and supporting head. However, the introduction of supporting head influences the dynamic characteristics of the tool-workpiece system. Essentially, the measured raw signal contains more coupled components and shows more oscillatory and aperiodic behaviors. Therefore, it is difficult to identify the mirror milling chatter using the monitoring signals. Comparing with traditional indicators, Q-factor can be used to describe the machining state in the view of signal oscillatory behavior, which is suitable for chatter-related component extraction in thin-walled part machining. In this paper, chatter-related signal component identification and diagnosis of thin-walled parts based on signal Q-factor and support vector machine (SVM) is proposed. The frequency band with maximal variation of Q-factors is taken as the chatter-related signal component. Using the feature vector constructed by Q-factors and power spectrum values of the determined frequency band, the SVM is used for milling state diagnosis. The prediction accuracy is much higher than the other frequency band and traditional indicators. It indicates the effectiveness of the proposed mirror machining chatter identification method.

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  1. 1.

    Liu HB, Wang YQ, Jia ZY, Guo DM (2015) Integration strategy of on-machine measurement (OMM) and numerical control (NC) machining for the large thin-walled parts with surface correlative constraint. Int J Adv Manuf Technol 80(9–12):1721–1731

  2. 2.

    Lan J, Lin B, Huang T, Xiao JL, Zhang XF, Fei JX (2017) Path planning for support heads in mirror-milling machining system. Int J Adv Manuf Technol 91(1):617–628

  3. 3.

    Fang B, Devor RE, Kapoor SG (2001) An elastodynamic model of frictional contact and its influence on the dynamics of a workpiece-fixture system. J Manuf Sci Eng 123(3):481–489

  4. 4.

    Kolluru K, Axinte D (2013) Coupled interaction of dynamic responses of tool and workpiece in thin wall milling. J Mater Process Technol 213(9):1565–1574

  5. 5.

    Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51(5):363–376

  6. 6.

    Liu T, Yan S, Zhang W (2016) Time–frequency analysis of nonstationary vibration signals for deployable structures by using the constant-Q nonstationary gabor transform. Mech Syst Signal Process 75:228–244

  7. 7.

    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

  8. 8.

    Bin GF, Gao JJ, Li XJ, Dhillon BS (2012) Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech Syst Signal Process 27:696–711

  9. 9.

    Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221

  10. 10.

    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):3339–3348

  11. 11.

    Shao H, Shi X, Li L (2011) Power signal separation in milling process based on wavelet transform and independent component analysis. Int J Mach Tools Manuf 51(9):701–710

  12. 12.

    Hu CZ, Yang Q, Huang MY, Yan WJ (2017) Sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox. Iet Renew Power Gener 11(3):330–337

  13. 13.

    Zhong ZM, Chen J, Zhong P, Wu JB (2006) Application of the blind source separation method to feature extraction of machine sound signals. Int J Adv Manuf Technol 28(9):855–862

  14. 14.

    Plaza EG, López PJN (2017) Surface roughness monitoring by singular spectrum analysis of vibration signals. Mech Syst Signal Process 84:516–530

  15. 15.

    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

  16. 16.

    Huang P, Li J, Sun J, Zhou J (2012) Vibration analysis in milling titanium alloy based on signal processing of cutting force. Int J Adv Manuf Technol 64(5–8):613–621

  17. 17.

    Liu Y, Wang X, Lin J, Zhao W (2015) Early chatter detection in gear grinding process using servo feed motor current. Int J Adv Manuf Technol 83(9–12):1801–1810

  18. 18.

    Dron JP, Bolaers F, Rasolofondraibe l (2004) Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings. J Sound Vib 270(1–2):61–73

  19. 19.

    Al-Ghamd AM, Mba D (2006) A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process 20(7):1537–1571

  20. 20.

    Fu Y, Zhang Y, Zhou H, Li D, Liu H, Qiao H, Wang XQ (2016) Timely online chatter detection in end milling process. Mech Syst Signal Process 75:668–688

  21. 21.

    Wang YQ, Bo QL, Liu HB, Lian M, Wang F, Zhang J (2017) Full-oscillatory components decomposition from noisy machining vibration signals by minimizing the Q-factor variation. Trans Inst Meas Control 39(9):1313–1328

  22. 22.

    Sims ND (2005) The self-excitation damping ratio: a chatter criterion for time-domain milling simulations. J Manuf Sci Eng 127(3):433–445

  23. 23.

    Cai G, Chen X, He Z (2013) Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox. Mech Syst Signal Process 41(1–2):34–53

  24. 24.

    Selesnick IW (2011) Sparse signal representations using the tunable Q-factor wavelet transform. Proceedings of SPIE - the. Int Soc Opt Eng 8138(3):815–822

  25. 25.

    Wang WQ, Golnaraghi MF, Ismail F (2004) Prognosis of machine health condition using neuro-fuzzy systems. Mech Syst Signal Process 18(4):813–831

  26. 26.

    Neshat M, Adeli A, Sepidnam G, Sargolzaei M (2012) Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems. Int J Adv Manuf Technol 63(1):373–390

  27. 27.

    Peng Y, Dong M (2011) A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction. Mech Syst Signal Process 25(1):237–252

  28. 28.

    Kong D, Chen Y, Li N (2017) Hidden semi-Markov model-based method for tool wear estimation in milling process. Int J Adv Manuf Technol 92(9):3647–3657

  29. 29.

    Yang Y, Yu D, Cheng J (2007) A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement 40(9–10):943–950

  30. 30.

    Tan F, Yin M, Wang L, Yin G (2018) Spindle thermal error robust modeling using LASSO and LS-SVM. Int J Adv Manuf Technol 94(5):2861–2874

  31. 31.

    Bhat NN, Dutta S, Vashisth T, Pal S, Pal SK, Sen R (2016) Tool condition monitoring by SVM classification of machined surface images in turning. Int J Adv Manuf Technol 83(9):1487–1502

  32. 32.

    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

  33. 33.

    Polito F, Petri A, Pontuale G, Dalton F (2010) Analysis of metal cutting acoustic emissions by time series models. Int J Adv Manuf Technol 48(9):897–903

  34. 34.

    Aghdam BH, Vahdati M, Sadeghi MH (2015) Vibration-based estimation of tool major flank wear in a turning process using ARMA models. Int J Adv Manuf Technol 76(9):1631–1642

  35. 35.

    Mosallam A, Medjaher K, Zerhouni N (2013) Nonparametric time series modelling for industrial prognostics and health management. Int J Adv Manuf Technol 69(5):1685–1699

  36. 36.

    Hoell S, Omenzetter P (2016) Optimal selection of autoregressive model coefficients for early damage detectability with an application to wind turbine blades. Mech Syst Signal Process 70-71:557–577

  37. 37.

    Huang W, Sun H, Wang W (2017) Resonance-based sparse signal decomposition and its application in mechanical fault diagnosis: a review. Sensors 17(6)

  38. 38.

    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

  39. 39.

    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

  40. 40.

    Gryllias KC, Antoniadis IA (2012) A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Eng Appl Artif Intell 25(2):326–344

  41. 41.

    Park CH, Lee M (2009) A SVM-based discretization method with application to associative classification. Expert Syst Appl 36(3:4784–4787

  42. 42.

    Faruto Li Y (2009) LIBSVM-farutoUltimateVersion, a toolbox with implements for support vector machines based on libsvm. Software available at

  43. 43.

    Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at

  44. 44.

    Luo J, Yu D, Liang M (2013) A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform. Meas Sci Technol 24(5):055009

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This work is supported by National Program on Key Basic Research Project (Grant No. 2014CB046604) and Science Challenge Project (Grant No. JCKY2016212A506-0201).

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Correspondence to Haibo Liu.

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Wang, Y., Bo, Q., Liu, H. et al. Mirror milling chatter identification using Q-factor and SVM. Int J Adv Manuf Technol 98, 1163–1177 (2018).

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  • Mirror milling
  • Chatter identification
  • Signal Q-factor
  • Support vector machine (SVM)
  • Thin-walled parts