Chatter Analysis and Stability Prediction of Milling Tool Based on Zero-Order and Envelope Methods for Real-Time Monitoring and Compensation

  • Wen-Yang ChangEmail author
  • Chung-Cheng Chen
  • Sheng-Jhih Wu
Regular Paper


The artificial intelligence means that it can autonomously determine the cutting situations regardless any cutting states and change them automatically as required. Regenerative chatter is an instability occurrence during CNC machining operation that must be avoided for high accuracy and greater surface manufactures. In this paper, an artificial intelligence based on zero-order and enveloped method is use for the chatter analysis and stability prediction of milling tool in real-time and on-line compensations. In order to measure the phase shift of harmonic frequency for real-time in cutting process, two three-axis accelerometers are installed at the bottom of the workpiece and at the above of the spindle to collect the vibration signal. Experimental results showed that the phase shift of regenerative chatter is higher than unchartered. The stable chatter signals of time domain vibration according to stability lobe diagram have low amplitude of vibration. This was confirmed that characteristic marks of chatter vibrations have higher amplitude level signal in the experimental test. In addition, this study developed a chatter prediction system for on-line calculation and real-time monitoring and compensation. The modal parameters of the chatter analysis and stability prediction system like natural frequencies, damping, and residues must also be identified automatically.


Chatter prediction On-line monitoring Phase shift G-magnitude Zero-order 

List of Symbols

\( v_{j} \left( {t - T} \right) \)

Vibration vector in present period

\( v_{j} \left( t \right) \)

Vibration vector in previous period

g(ϕj), A(t)

Unit step function and periodic vector over a tooth period

\( \emptyset_{j} \)

Rotational angle of jth tooth measured CW from normal y axis

\( S_{f} \)

Feed rate per tooth


Milling tooth period

dFtj, dFrj

Tangential and radial cutting forces

\( \phi_{st} , \phi_{ex} \)

Start and exit immersion angles of milling tooth

Kt, Kr

Cutting coefficients along tangential and radial directions

ϕp, N

Cutter pitch angle and number of milling teeth

Ps, Pw

Spindle phase and workpiece phase



This work was partially supported by the Ministry of Science and Technology, under Grant Nos. MOST 106-2221-E-150-020, MOST 107-2221-E-150-019, 106-AF-090, 107AF036, and 107B1034.


  1. 1.
    Tangjitsitcharoen, S., & Moriwaki, T. (2008). Intelligent monitoring and identification of cutting states of chips and chatter on CNC turning machine. Journal of Manufacturing Processes, 10(1), 40–46.Google Scholar
  2. 2.
    Chao, S., & Altintas, Y. (2016). Chatter free tool orientations in 5-axis ball-end milling. International Journal of Machine Tools and Manufacture, 106, 89–97.Google Scholar
  3. 3.
    Ozoegwu, C. G., Ofochebe, S. M., & Omenyi, S. N. (2016). A method of improving chatter-free conditions with combined-mode milling. Journal of Manufacturing Processes, 21, 1–13.Google Scholar
  4. 4.
    Budak, E., & Altintas, Y. (1998). Analytical prediction of chatter stability in milling—Part I: General formulation. Journal of Dynamic Systems, Measurement, and Control, 120(1), 22–30.Google Scholar
  5. 5.
    Munoa, J., Beudaert, X., Dombovari, Z., Altintas, Y., Budak, E., Brecher, C., et al. (2016). Chatter suppression techniques in metal cutting. CIRP Annals—Manufacturing Technology, 65(2), 785–808.Google Scholar
  6. 6.
    Chang, W.-Y., & Sheng-Jhih, W. (2016). Big data analysis of a mini three-axis CNC machine tool based on the tuning operation of controller parameters. The International Journal of Advanced Manufacturing Technology, 99(5–8), 1077–1083.Google Scholar
  7. 7.
    Jeong, G.-B., Kim, D. H., & Jang, D. Y. (2005). Real time monitoring and diagnosis system development in turning through measuring a roundness error based on three-point method. International Journal of Machine Tools and Manufacture, 45, 1494–1503.Google Scholar
  8. 8.
    Toh, C. K. (2004). Vibration analysis in high speed rough and finish milling hardened steel. Journal of Sound and Vibration, 278, 101–115.Google Scholar
  9. 9.
    Cao, H., Lei, Y., & He, Z. (2013). Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform. International Journal of Machine Tools and Manufacture, 69, 11–19.Google Scholar
  10. 10.
    Grossi, N., Sallese, L., Scippa, A., & Campatelli, G. (2017). Improved experimental–analytical approach to compute speed-varying tool-tip FRF. Precision Engineering, 48, 114–122.Google Scholar
  11. 11.
    Xiaoliang Jin and Yusuf Altintas. (2013). Chatter stability model of micro-milling with process damping. Journal of Manufacturing Science and Engineering, 135(3), 031011.Google Scholar
  12. 12.
    Budak, E., & Altintas, Y. (1998). Analytical prediction of chatter stability in milling—Part II: Application of the general formulation to common milling systems. Journal of Dynamic Systems, Measurement, and Control, 120(1), 31–36.Google Scholar
  13. 13.
    Budak, E., & Altintas, Y. (1995). Analytical prediction of stability lobes in milling. Annals of the CIRP, 44(1), 357–362.Google Scholar
  14. 14.
    Acary, V., Brogliato, B., & Orlov, Y. V. (2012). Chattering-free digital sliding-mode control with state observer and disturbance rejection. IEEE Transactions on Automatic Control, 57(5), 1087–1101.MathSciNetzbMATHGoogle Scholar
  15. 15.
    Jin, G., Zhang, Q., Hao, S., & Xie, Q. (2013). Stability prediction of milling process with variable pitch cutter. Mathematical Problems in Engineering, 2013, Article ID 932013.Google Scholar
  16. 16.
    Insperger, T., Stepan, G., Bayly, P. V., & Mann, B. P. (2003). Multiple chatter frequencies in milling processes. Journal of Sound and Vibration, 262, 333–345.Google Scholar
  17. 17.
    Altintas, Y. (2012). Manufacturing automation: Metal cutting mechanics, machine tool vibrations (p. 171). Cambridge: Cambridge University Press.Google Scholar
  18. 18.
    Lacerda, H. B., & Lima, V. T. (2004). Evaluation of cutting forces and prediction of chatter vibrations in milling. The Journal of the Brazilian Society of Mechanical Sciences and Engineering, 26(1), 74–81.Google Scholar

Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  • Wen-Yang Chang
    • 1
    • 2
    Email author
  • Chung-Cheng Chen
    • 1
  • Sheng-Jhih Wu
    • 3
  1. 1.Department of Mechanical and Computer-Aided EngineeringNational Formosa UniversityYunlinTaiwan
  2. 2.Smart Machine and Intelligent Manufacturing Research CenterNational Formosa UniversityYunlinTaiwan
  3. 3.Department of Power Mechanical EngineeringNational Formosa UniversityYunlinTaiwan

Personalised recommendations