Skip to main content
SpringerLink
Log in

ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Suppression of machining chatter during milling processes is of great significance for surface finish and tool life. In this paper, a smart CNC milling system integrating the function of signal processing, monitoring, and intelligent control is presented with the aim of real-time chatter monitoring and suppression. The algorithm of estimation of signal parameters via rotational invariance techniques (ESPRIT) is adopted to extract the frequency characteristics of acceleration signals, and then, cutting state is categorized as stable state, chatter germination state, and chatter state based on amplitude-frequency characteristics of identified acceleration signals. The model of chatter identification is acquired by training a hidden Markov model (HMM), which combines acceleration signals and labeled cutting state. To implement real-time chatter suppression, the algorithm of fuzzy control is integrated into a smart CNC kernel to determine the relationship between cutting force and spindle speed. Furthermore, spindle speed of machine tool could be adjusted timely in the presented system once the chatter is identified. Finally, the effectiveness of the proposed real-time chatter monitoring and suppression system is experimentally validated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Yang Y, Zhang WH, Wan M, Ma YC (2013) A solid trimming method to extract cutter-workpiece engagement maps for multi-axis milling. Int J Adv Manuf Technol 68(9–12):2801–2813. doi:10.1007/s00170-013-4786-2

    Article  Google Scholar 

  2. Tlusty J (1986) Dynamics of high-speed milling. J Eng Ind Trans ASME 108(2):59–67. doi:10.1115/1.3187052

    Article  Google Scholar 

  3. Altintas Y, Budak E (1995) Analytical prediction of stability lobes in milling. CIRP Ann-Manuf Technol 44(1):357–362. doi:10.1016/S0007-8506(07)62342-7

    Article  Google Scholar 

  4. He C, Xing JC, Li JL, Yang QL, Wang RH (2015) A new wavelet threshold determination method considering interscale correlation in signal denoising. Math Probl Eng 2015:1–9. doi:10.1155/2015/280251

    Google Scholar 

  5. Yusoff AR, Turner S, Taylor CM, Sims ND (2010) The role of tool geometry in process damped milling. Int J Adv Manuf Technol 50(9-12):883–895. doi:10.1007/s00170-010-2586-6

    Article  Google Scholar 

  6. Al-Regib E, Ni J (2010) Chatter detection in machining using nonlinear energy operator. J Dyn Syst Meas Control Trans ASME 132(3):1–4. doi:10.1115/1.4001331

    Article  Google Scholar 

  7. Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vibr 312(4):672–693. doi:10.1016/j.jsv.2007.11.006

    Article  Google Scholar 

  8. Kuljanic E, Totis G, Sortino M (2009) Development of an intelligent multisensor chatter detection system in milling. Mech Syst Signal Proc 23(5):1704–1718. doi:10.1016/j.ymssp.2009.01.003

    Article  Google Scholar 

  9. van Dijk NJM, Doppenberg EJJ, Faassen RPH, van de Wouw N, Oosterling JAJ, Nijmeijer H (2010) Automatic in-process chatter avoidance in the high-speed milling process. J Dyn Syst Meas Control Trans ASME 132(3):333–342. doi:10.1115/1.4000821

    Article  Google Scholar 

  10. Ma L, Melkote SN, Castle JB (2013) MA model-based computationally efficient method for on-line detection of chatter in milling. J Manuf Sci Eng 135(3):1–11. doi:10.1115/MSEC2013-1031

    Article  Google Scholar 

  11. HB Sun, Zhang XZ, Wang JY (2016) Online machining chatter forecast based on improved local mean decomposition. Int J Adv Manuf Technol 84(5):1045–1056. doi:10.1007/s00170-015-7785-8

    Google Scholar 

  12. Kang J, Hu HY, Xiao SZ (2011) Research on pattern recognition of chatter in grinding based on HMM. Inform Int Interdiscipl J 14(8):2563–2573

    Google Scholar 

  13. Olgac N, Hosek M (1998) A new perspective and analysis for regenerative machine tool chatter. Int J Mach Tools Manuf 38(7):783–798. doi:10.1016/S0890-6955(97)00062-X

    Article  Google Scholar 

  14. Morita H, Yamashita T (2012) Racing and visualizing variation of chatter for in-process identification of preferred spindle speeds. 3rd CIRP conference on process machine interactions. Procedia CIRP 4(11):11–16. doi:10.1016/j.procir.2012.10.003

    Article  Google Scholar 

  15. Kim DH, Song JY, Cha SK, Son H (2011) The development of embedded device to detect chatter vibration in machine tools and CNC-based autonomous compensation. J Mech Sci Technol 25(10):2623–2630. doi:10.1007/s12206-011-0737-9

    Article  Google Scholar 

  16. Smith S, Tlusty J (1992) Stabilizing chatter by automatic spindle speed regulation. CIRP Ann-Manuf Technol 41(1):433–436. doi:10.1016/S0007-8506(07)61238-4

    Article  Google Scholar 

  17. Delio T, Tlusty J, Smith S (1992) Use of audio signals for chatter detection and control. J Manuf Sci Eng 114(2):146–157. doi:10.1115/1.2899767

    Article  Google Scholar 

  18. Smith S, Delio T (1992) Sensor-based chatter detection and avoidance by spindle speed selection. J Dyn Syst Meas Control Trans ASME 114(3):486–492. doi:10.1115/1.2897373

    Article  Google Scholar 

  19. Altintas Y, Weck M (2004) Chatter stability of metal cutting and grinding. CIRP Ann-Manuf Technol 53(2):619–642. doi:10.1016/S0007-8506(07)60032-8

    Article  Google Scholar 

  20. Insperger T, Stepan G, Bayly PV, Mann BP (2003) Multiple chatter frequencies in milling processes. J Sound Vibr 262(2):333–345. doi:10.1016/S0022-460X(02)01131-8

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechatronics Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China

    Zhenyu Han, Hongyu Jin, Dedong Han & Hongya Fu

Authors
  1. Zhenyu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongyu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dedong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongya Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongya Fu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, Z., Jin, H., Han, D. et al. ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system. Int J Adv Manuf Technol 89, 2731–2746 (2017). https://doi.org/10.1007/s00170-016-9863-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9863-y

Keywords

Search

Navigation