Skip to main content
SpringerLink
Log in

Identification of milling chatter based on a novel frequency-domain search algorithm

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

Abstract

Chatter is a kind of self-excited vibration, which will result in very poor quality and dimensional accuracy on the machined surface, even a harmful effect on machining operation. A simple, reliable, and accurate chatter identification algorithm is crucial for taking control strategy before it is fully developed. This paper proposes a novel frequency-domain search (FDS) algorithm to identify the chatter during milling. An identification feature based on vibration base frequency is extracted according to the FDS algorithm, where the complicated signal processing algorithms are not needed before feature extraction. Compared with most of the existing identification features, the introduced feature does not need to set thresholds according to different machining conditions. Meanwhile, the feature extraction only needs a small amount of data to guarantee the timeliness of identification. Hammer test and milling experiments with various cutting parameters are carried out, and both force signal and vibration signal in the experiments are utilized to validate the effectiveness of the proposed algorithm. The results show that the proposed algorithm can identify the milling chatter accurately, whether using force signal or vibration signal, even slight chatter in the initial machining stage can be identified. Furthermore, the research reveals that dominant chatter frequencies appear around the natural frequency of the spindle-tool system but do not exactly equal to its natural frequency. The chatter frequencies are found to be determined by the combination of natural characteristics of the system and cutting conditions.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Abbreviations

a e :

Radial depth of cut (mm)

a p :

Axial depth of cut (mm)

f C :

Vibration base frequency (Hz)

f cfr :

Searching range of the proposed algorithm

\( {f}_{\mathrm{cfr}}^a \) :

Left interval of the searching range (Hz)

\( {f}_{\mathrm{cfr}}^b \) :

Right interval of the searching range (Hz)

f d :

Difference between fIMF and fTPF (Hz)

f IMF :

Identified maximum frequency (Hz)

f MCF :

Multiple chatter frequencies (Hz)

f SRF :

Spindle rotation frequency (Hz)

f STF :

Surface topography frequency (Hz)

f TPF :

Tooth pass frequency (Hz)

f z :

Feed per tooth (Hz)

Fx, Fy :

Cutting force signal in x and y directions, respectively (N)

k b :

Multiple of initial search frequency

L w :

Measured length of surface profile (mm)

n :

Spindle speed (rpm)

n w :

Number of the corrugations in the scope of Lw

N t :

Number of teeth

Sx, Sy :

Spectrum of cutting force signal of x and y directions, respectively

References

  1. Yang K, Wang GF, Dong Y, Zhang QB, Sang LL (2019) Early chatter identification based on an optimized variational mode decomposition. Mech Syst Signal Pr 115:238–254. https://doi.org/10.1016/j.ymssp.2018.05.052

    Article  Google Scholar 

  2. Aslan D, Altintas Y (2018) On-line chatter detection in milling using drive motor current commands extracted from CNC. Int J Mach Tool Manu 132:64–80. https://doi.org/10.1016/j.ijmachtools.2018.04.007

    Article  Google Scholar 

  3. Catania G, Mancinelli N (2011) Theoretical-experimental modeling of milling machines for the prediction of chatter vibration. Int J Mach Tool Manu 51:339–348. https://doi.org/10.1016/j.ijmachtools.2018.04.007

    Article  Google Scholar 

  4. Kayhan M, Budak E (2009) An experimental investigation of chatter effects on tool life. P I Mech Eng B-J Eng 223(11):1455–1463. https://doi.org/10.1243/09544054JEM1506

    Article  Google Scholar 

  5. Li ZQ, Wang ZK, Shi XF (2017) Fast prediction of chatter stability lobe diagram for milling process using frequency response function or modal parameters. Int J Adv Manuf Technol 89:2603–2612. https://doi.org/10.1007/s00170-016-9959-4

    Article  Google Scholar 

  6. Grossi N, Montevecchi F, Sallese L, Scippa A, Campatelli G (2017) Chatter stability prediction for high-speed milling through a novel experimental-analytical approach. Int J Adv Manuf Technol 89:2587–2601. https://doi.org/10.1007/s00170-016-9832-5

    Article  Google Scholar 

  7. Graham E, Mehrpouya M, Park SS (2013) Robust prediction of chatter stability in milling based on the analytical chatter stability. J Manuf Process 15:508–517. https://doi.org/10.1016/j.jmapro.2013.08.005

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Najafi B, Hakim H (1992) A comparative study of non-parametric spectral estimators for application in machining vibration analysis. Mech Syst Signal Pr 6:551–574. https://doi.org/10.1016/0888-3270(92)90049-O

    Article  Google Scholar 

  10. Lamraoui M, Thomas M, El Badaoui M, Girardin F (2014) Indicators for monitoring chatter in milling based on instantaneous angular speeds. Mech Syst Signal Pr 44:72–85. https://doi.org/10.1016/j.ymssp.2013.05.002

    Article  Google Scholar 

  11. Suh CS, Khurjekar PP, Yang B (2002) Characterisation and identification of dynamic instability in milling operation. Mech Syst Signal Pr 16(5):853–872. https://doi.org/10.1006/mssp.2002.1497

    Article  Google Scholar 

  12. Feng JL, Sun ZL, Jiang ZH, Yang L (2016) Identification of chatter in milling of Ti-6Al-4V titanium alloy thin-walled workpieces based on cutting force signals and surface topography. Int J Adv Manuf Technol 82:1909–1920. https://doi.org/10.1007/s00170-015-7509-0

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Niu JC, Ning GC, Shen YJ, Yang SP (2019) Detection and identification of cutting chatter based on improved variational nonlinear chirp mode decomposition. Int J Adv Manuf Technol 104:2567–2578. https://doi.org/10.1007/s00170-019-04035-z

    Article  Google Scholar 

  15. Gao J, Song QH, Liu ZQ (2018) Chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT. Int J Adv Manuf Technol 98:699–713. https://doi.org/10.1007/s00170-018-2306-1

    Article  Google Scholar 

  16. Ye J, Feng PF, Xu C, Ma Y, Huang SG (2018) A novel approach for chatter online monitoring using coefficient of variation in machining process. Int J Adv Manuf Technol 96:287–297. https://doi.org/10.1007/s00170-017-1544-y

    Article  Google Scholar 

  17. Ji YJ, Wang XB, Liu ZB, Yan ZH, Jiao L, Wang DQ, Wang JQ (2017) EEMD-based online milling chatter detection by fractal dimension and power spectral entropy. Int J Adv Manuf Technol 92:1185–1200. https://doi.org/10.1007/s00170-017-0183-7

    Article  Google Scholar 

  18. Fu Y, Zhang Y, Zhou H, Li DQ, Liu HQ, Qiao HY, Wang XQ (2016) Timely online chatter detection in end milling process. Mech Syst Signal Pr 75:668–688. https://doi.org/10.1016/j.ymssp.2016.01.003

    Article  Google Scholar 

  19. Cao HR, Zhou K, Chen XF, Zhang XW (2017) Early chatter detection in end milling based on multi-feature fusion and 3σ criterion. Int J Adv Manuf Technol 92:4387–4397. https://doi.org/10.1007/s00170-017-0476-x

    Article  Google Scholar 

  20. Li K, He SP, Luo B, Li B, Liu HQ, Mao XY (2019) Online chatter detection in milling process based on VMD and multiscale entropy. Int J Adv Manuf Technol 105:5009–5022. https://doi.org/10.1007/s00170-019-04478-4

    Article  Google Scholar 

  21. Chen GS, Zheng QZ (2018) Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination. Int J Adv Manuf Technol 95:775–784. https://doi.org/10.1007/s00170-017-1242-9

    Article  Google Scholar 

  22. Cao HR, Lei YG, He ZJ (2013) Chatter identification in end milling process using wavelet packets and Hilbert-Huang transform. Int J Mach Tool Manu 69:11–19. https://doi.org/10.1016/j.ijmachtools.2013.02.007

    Article  Google Scholar 

  23. Choi T, Shin YC (2003) Online chatter detection using wavelet-based parameter estimation. J Manuf Sci E-T ASME 125(1):21–28. https://doi.org/10.1115/1.1531113

    Article  Google Scholar 

  24. Al-Regib E, Ni J (2010) Chatter detection in machining using nonlinear energy operator. J Dyn Syst-T ASME 132:034502(1-4). https://doi.org/10.1115/1.4001331

    Article  Google Scholar 

  25. Caliskan H, Kilic ZM, Altintas Y (2018) Online energy-based milling chatter detection. J Manuf Sci E-T ASME 140:111012 (1-12). https://doi.org/10.1115/1.4040617

  26. Liu HB, Bo QL, Zhang H, Wang YQ (2018) Analysis of Q-factor’s identification ability for thin-walled part flank and mirror milling chatter. Int J Adv Manuf Technol 99:1673–1686. https://doi.org/10.1007/s00170-018-2580-y

    Article  Google Scholar 

  27. Wang GF, Dong HY, Guo YJ, Ke YL (2018) Early chatter identification of robotic boring process using measured force of dynamometer. Int J Adv Manuf Technol 94:1243–1252. https://doi.org/10.1007/s00170-017-0941-6

    Article  Google Scholar 

  28. Zhang Z, Li HG, Meng G, Tu XT, Cheng CM (2016) Chatter detection in milling process based on the energy entropy of VMD and WPD. Int J Mach Tool Manu 108:106–112. https://doi.org/10.1016/j.ijmachtools.2016.06.002

    Article  Google Scholar 

  29. Berger B, Belai C, Anand D (2003) Chatter identification with mutual information. J Sound Vib 267(1):178–186. https://doi.org/10.1016/S0022-460X(03)00067

    Article  Google Scholar 

  30. Schmitz TL (2003) Chatter recognition by a statistical evaluation of the synchronously sampled audio signal. J Sound Vib 262(3):721–730. https://doi.org/10.1016/S0022-460X(03)00119-6

    Article  Google Scholar 

  31. Rusinek R, Lajmert P, Kecik K, Kruszynski B, Warminski J (2015) Chatter identification methods on the basis of time series measured during titanium superalloy milling. Int J Mech Sci 99:196–207. https://doi.org/10.1016/j.ijmecsci.2015.05.013

    Article  Google Scholar 

  32. Shao YM, Deng X, Yuan YL, Mechefske CK, Chen ZG (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. https://doi.org/10.1007/s12206-014-0106-6

    Article  Google Scholar 

  33. Ma L, Melkote SN, Castle JB (2013) A model based computationally efficient method for on-line detection of chatter in milling. J Manuf Sci E-T ASME 135(3):1–11. https://doi.org/10.1115/MSEC2013-1031

    Article  Google Scholar 

  34. 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-T ASME 132(3):333–342. https://doi.org/10.1115/1.4000821

    Article  Google Scholar 

  35. Wan SK, Li XH, Chen W, Hong J (2018) 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

    Article  Google Scholar 

  36. Insperger T, Stépán G, Bayly PV, Mann BP (2003) Multiple chatter frequencies in milling processes. J Sound Vib 262:333–345. https://doi.org/10.1016/S0022-460X(02)01131-8

    Article  Google Scholar 

  37. Dombovari Z, Iglesias A, Zatarain M, Insperger T (2011) Prediction of multiple dominant chatter frequencies in milling process. Int J Mach Tool Manu 51:457–464. https://doi.org/10.1016/j.ijmachtools.2011.02.002

    Article  Google Scholar 

  38. Wang GF, Peng DB, Qin XD, Cui YH (2012) An improved dynamic milling force coefficients identification method considering edge force. J Mech Sci Technol 26(5):1585–1590. https://doi.org/10.1007/s12206-012-0306-x

    Article  Google Scholar 

  39. Ding Y, Zhu LM, Zhang XJ, Ding H (2010) A full-discretization method for prediction of milling stability. Int J Mach Tool Manu 50:502–509. https://doi.org/10.1016/j.ijmachtools.2010.01.003

    Article  Google Scholar 

Download references

Acknowledgments

This project is supported by the Natural Science Foundation of Tianjin (No. 18JCQNJC75600), National Natural Science Foundation of China (No. 51705362), and Science & Technology Development Fund of Tianjin Education Commission for Higher Education (No. 2017KJ081).

Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong University, Tianjin, 300387, China

    Liu Chang, Xu Weiwei & Gao Lei

  2. School of Mechanical Engineering, Tiangong University, Tianjin, 300387, China

    Liu Chang, Xu Weiwei & Gao Lei

Authors
  1. Liu Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu Weiwei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gao Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liu Chang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, L., Weiwei, X. & Lei, G. Identification of milling chatter based on a novel frequency-domain search algorithm. Int J Adv Manuf Technol 109, 2393–2407 (2020). https://doi.org/10.1007/s00170-020-05789-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05789-7

Keywords

Search

Navigation