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A synchroextracting-based method for early chatter identification of robotic drilling process

  • Jianfeng Tao
  • Chengjin QinEmail author
  • Chengliang Liu
ORIGINAL ARTICLE
  • 62 Downloads

Abstract

The robotic drilling process has shown its high industrial application potential for millions of holes in the aviation manufacturing. However, due to the relatively low stiffness of the serial robot manipulator, the robotic drilling system is more prone to chatter, leading to poor surface roughness, unbearable noise, and even causing severe damage to the end effector spindle. Consequently, it is of vital significance to identify and suppress this undesirable vibration. In this paper, a synchroextracting-based method is proposed for the early chatter detection of robotic drilling operations. The proposed algorithm is implemented through the following steps. First, the accurate time-frequency representation of the measured vibration signal is acquired by employing the synchroextracting transform (SET), which has high energy concentration and robustness to measurement noise. Second, the whole signal is divided into a finite number of frequency bands, and the corresponding sub-signal for each frequency band can be reconstructed by retaining the maximum coefficient of the SET. Finally, working as the chatter indicator, the statistical energy entropy is utilized to capture the inhomogeneous variation of energy distribution during the chatter transition process. Robotic drilling experiments with different cutting conditions were conducted to verify the effectiveness of the proposed chatter identification method. The results demonstrate that the presented synchroextracting-based algorithm can identify the chatter at an early stage, which is useful for the subsequent chatter suppression.

Keywords

Robotic drilling process Chatter identification Synchroextracting-based method Time-frequency representation Energy entropy 

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Notes

Funding information

This work was partially supported by the National Key Research and Development Program of China (Grant No. 2017YFB1302601), the special scientific research project of Shanghai Tunnel Engineering Co. Ltd. (Grant No. 2017-SK-09) and the project of Shanghai Science and Technology Commission (Grant No. 17511109203).

References

  1. 1.
    Chen Y, Dong F (2013) Robot machining: recent development and future research issues. Int J Adv Manuf Technol 66(9–12):1489–1497CrossRefGoogle Scholar
  2. 2.
    Bi S, Liang J (2011) Robotic drilling system for titanium structures. Int J Adv Manuf Technol 54:767–774CrossRefGoogle Scholar
  3. 3.
    Liang J (2015) A research on the mounted configuration of end-effector for robotic drilling. Robotica 33(10):2156–2165CrossRefGoogle Scholar
  4. 4.
    Bu Y, Liao WH, Tian W, Zhang L, Li DW (2017) Modeling and experimental investigation of Cartesian compliance characterization for drilling robot. Int J Adv Manuf Technol 91(9–12):3253–3264CrossRefGoogle Scholar
  5. 5.
    Pan Z, Zhang H, Zhu Z, Wang J (2006) Chatter analysis of robotic machining process. J Mater Process Technol 173:301–309CrossRefGoogle Scholar
  6. 6.
    Mejri S, Gagnol V, Le TP, Sabourin L, Paultre P, Ray P (2016) Dynamic characterization of machining robot and stability analysis. Int J Adv Manuf Technol 82:351–359CrossRefGoogle Scholar
  7. 7.
    Wang G, Dong H, Guo Y, Ke Y (2017) Chatter mechanism and stability analysis of robotic boring. Int J Adv Manuf Technol 91:411–421CrossRefGoogle Scholar
  8. 8.
    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):785–808CrossRefGoogle Scholar
  9. 9.
    Quintana G, Stepan CJ (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51(5):363–376CrossRefGoogle Scholar
  10. 10.
    Kilic ZM, Altintas Y (2016) Generalized mechanics and dynamics of metal cutting operations for unified simulations. Int J Mach Tools Manuf 104:1–13CrossRefGoogle Scholar
  11. 11.
    Altintas Y, Stepan G, Merdol D, Dombovari Z (2008) Chatter stability of milling in frequency and discrete time domain. CIRP J Manuf Sci Technol 1(1):35–44CrossRefGoogle Scholar
  12. 12.
    Qin CJ, Tao JF, Li L, Liu CL (2017) An Adams-Moulton-based method for stability prediction of milling processes. Int J Adv Manuf Technol 89(9–12):3049–3058CrossRefGoogle Scholar
  13. 13.
    Qin CJ, Tao JF, Liu CL (2017) Stability analysis for milling operations using an Adams-Simpson-based method. Int J Adv Manuf Technol 92(1–4):969–979CrossRefGoogle Scholar
  14. 14.
    Lauro CH, Brandão LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes—a review. Measurement 58:73–86CrossRefGoogle 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.
    Qin CJ, Tao JF, Liu CL (2018) A predictor-corrector-based holistic-discretization method for accurate and efficient milling stability analysis. Int J Adv Manuf Technol 96(5–8):2043–2054CrossRefGoogle Scholar
  17. 17.
    Ding H, Ding Y, Zhu LM (2012) On time-domain methods for milling stability analysis. Chin Sci Bull 57(33):4336–4345CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    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
  20. 20.
    Sun YX, Xiong ZH (2016) An optimal weighted wavelet packet entropy method with application to real-time chatter detection. IEEE-ASME T Mech 21(4):2004–2014CrossRefGoogle Scholar
  21. 21.
    Lamraoui M, Thomas M, ElBadaoui M (2014) Cyclostationarity approach for monitoring chatter and tool wear in high speed milling. Mech Syst Signal Process 44(1–2):177–198CrossRefGoogle Scholar
  22. 22.
    Wan SK, Li XH, Chen W, Hong J (2017) Investigation on milling chatter identification at early stage with variance ratio and Hilbert–Huang transform. Int J Adv Manuf Technol 95:3563–3573.  https://doi.org/10.1007/s00170-017-1410-y CrossRefGoogle Scholar
  23. 23.
    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):613–621Google Scholar
  24. 24.
    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
  25. 25.
    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):1–15Google Scholar
  26. 26.
    Wang G, Dong H, Guo Y, Ke Y (2018) Early chatter identification of robotic boring process using measured force of dynamometer. Int J Adv Manuf Technol 94(1–4):1243–1252CrossRefGoogle Scholar
  27. 27.
    Schmitz TL (2003) Chatter recognition by a statistical evaluation of the synchronously sampled audio signal. J Sound Vib 262(3):721–730CrossRefGoogle Scholar
  28. 28.
    Thaler T, Potočnik P, Bric I, Govekar E (2014) Chatter detection in band sawing based on discriminant analysis of sound features. Appl Acoust 77:114–121CrossRefGoogle Scholar
  29. 29.
    Cao H, Yue Y, Chen X, Zhang X (2017) Chatter detection in milling process based on synchro squeezing transform of sound signals. Int J Adv Manuf Technol 89(9–12):2747–2755CrossRefGoogle Scholar
  30. 30.
    Liu H, Chen Q, Li B, Mao X, Mao K, Peng F (2011) On-line chatter detection using servo motor current signal in turning. Sci China Technol Sci 54(12):3119–3129CrossRefzbMATHGoogle Scholar
  31. 31.
    Liu Y, Wang X, Lin J, Zhao W (2016) Early chatter detection in gear grinding process using servo feed motor current. Int J Adv Manuf Technol 83:1801–1810CrossRefGoogle Scholar
  32. 32.
    Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4):672–693CrossRefGoogle Scholar
  33. 33.
    Tangjitsitcharoen S (2009) In-process monitoring and detection of chip formation and chatter for CNC turning. J Mater Process Technol 209(10):4682–4688CrossRefGoogle Scholar
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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 92:52–59CrossRefGoogle Scholar
  38. 38.
    Auger F, Flandrin P, Lin YT, McLaughlin S, Meignen S, Oberlin T, Wu HT (2013) Time-frequency reassignment and synchrosqueezing: an overview. IEEE Signal Process Mag 30(6):32–41CrossRefGoogle Scholar
  39. 39.
    Daubechies I, Lu J, H-T W (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Wang S, Chen X, Cai G, Chen B, Li X, He Z (2014) Matching demodulation transform and synchrosqueezing in time-frequency analysis. IEEE Trans Signal Process 62(1):69–84MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Oberlin T, Meignen S, Perrier V (2015) Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time-frequency representations. IEEE Trans Signal Process 63(5):1335–1344MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Peng ZK, Meng G, Chu FL, Lang ZQ, Zhang WM, Yang Y (2011) Polynomial chirplet transform with application to instantaneous frequency estimation. IEEE Trans Instrum Meas 60(9):1378–1384CrossRefGoogle Scholar
  43. 43.
    Yang Y, Zhang W, Peng Z, Meng G (2013) Multicomponent signal analysis based on polynomial chirplet transform. IEEE Trans Ind Electron 60(9):3948–3956CrossRefGoogle Scholar
  44. 44.
    Yang Y, Peng Z, Meng G, Zhang W (2012) Spline-kernelled chirplet transform for the analysis of signals with time-varying frequency and its application. IEEE Trans Ind Electron 59(3):1612–1621CrossRefGoogle Scholar
  45. 45.
    Wang S, Chen X, Li G, Li X, He Z (2014) Matching demodulation transform with application to feature extraction of rotor rub-impact fault. IEEE Trans Instrum Meas 63(5):1372–1383CrossRefGoogle Scholar
  46. 46.
    Yu G, Yu M, Xu C (2017) Synchroextracting transform. IEEE Trans Ind Electron 64(10):8042–8054CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.State Key Laboratory of Mechanical System and Vibration, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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