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Application of Combined Technique for Chatter Prediction in 5-Axis Milling

  • V. A. Kuts
  • S. M. Nikolaev
  • I. A. Kiselev
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

The technologies used in machining have been recently developing in a rapid way. Higher quality requirements, workpieces complexity, and appearance of new poor machinability materials cause the need to improve the existing methods and create new ones. One of the main problems, which limits machining efficiency, is self-excited vibrations. Such vibrations are mainly caused by the regenerative mechanism of machining workpiece surface. The regenerative mechanism appearance causes the substantial deterioration of the machined surface, tool and machine units wear. This issue is far more important while processing compliant parts made of poor machinability materials, for instance, blades or bling of gas turbine engines. The verification of the calculation-experimental method, which has been designed by the authors and allows to predict self-excited vibrations appearance in the tool/workpiece system and set more efficient machining modes, was carried out. The verification of the method was conducted for the 5-axis machining of the aluminum blade of a gas turbine engine model as an example. The blade milling dynamics modeling was made with a view to receiving workpiece acceleration signals with a varied rotational speed of the spindle. “The diagram of modes” was plotted as a result of the acceleration signals analysis. The mode in which a self-excited vibration will not appear can be found in this diagram. The test experiments were carried out to mill the blade model with chosen parameters. Finally, the authors compared the experiment and modeling results to check the methods.

Keywords

Chatter detection Self-excited vibrations Dynamics of milling Signal processing Singular spectrum analysis 

References

  1. 1.
    Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press, CambridgeGoogle Scholar
  2. 2.
    Van Dijk N (2011) Active chatter control in high-speed milling processes. Dissertation, Eindhoven University of TechnologyGoogle Scholar
  3. 3.
    Tlusty J (1993) High-speed machining. CIRP Ann Manuf Technol 42:733–738.  https://doi.org/10.1016/S0007-8506(07)62536-0CrossRefGoogle Scholar
  4. 4.
    Grzesik W (2008) Advanced machining processes of metallic materials: theory, modelling and applications. Elsevier, AmsterdamCrossRefGoogle Scholar
  5. 5.
    Altintaş Y, Budak E (1995) Analytical prediction of stability lobes in milling. CIRP Ann Manuf Technol 44:357–362.  https://doi.org/10.1016/S0007-8506(07)62342-7CrossRefGoogle Scholar
  6. 6.
    Altintas Y (2001) Analytical prediction of three dimensional chatter stability in milling. JSME Int J, Ser C 44:717–723CrossRefGoogle Scholar
  7. 7.
    Tlusty J, Polacek M (1963) The stability of machine tools against self-excited vibrations in machining. In: International research in production engineering, pp 465–474Google Scholar
  8. 8.
    Hanna NH, Tobias SA (1974) A theory of nonlinear regenerative chatter. ASME J Eng Ind 96:247–255.  https://doi.org/10.1115/1.3438305CrossRefGoogle Scholar
  9. 9.
    Shi HM, Tobias SA (1984) Theory of finite amplitude machine tool instability. Int J Mac Tool Des Res 24:45–69.  https://doi.org/10.1016/0020-7357(84)90045-3CrossRefGoogle Scholar
  10. 10.
    Altintas Y, Chan PK (1992) In-process detection and suppression of chatter in milling. Int J Mach Tools Manuf 32:329–347CrossRefGoogle Scholar
  11. 11.
    Li XQ, Wong YS, Nee AY (1997) Tool wear and chatter detection using the coherence function of two crossed accelerations. Int J Mach Tools Manuf 37:425–435.  https://doi.org/10.1016/S0890-6955(96)00030-2CrossRefGoogle Scholar
  12. 12.
    Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210:713–719.  https://doi.org/10.1016/j.jmatprotec.2009.11.007CrossRefGoogle Scholar
  13. 13.
    Choi T, Shin YC (2003) On-line chatter detection using wavelet-based parameter estimation. Trans Am Soc Mech Eng J Manuf Sci Eng 125:21–28.  https://doi.org/10.1115/1.1531113CrossRefGoogle Scholar
  14. 14.
    Yoon MC, Chin DH (2005) Cutting force monitoring in the endmilling operation for chatter detection. Proc Inst Mech Eng Part B J Eng Manuf 219:455–465CrossRefGoogle Scholar
  15. 15.
    Schmitz TL, Medicus K, Dutterer B (2002) Exploring once-per-revolution audio signal variance as a chatter indicator. Mach Sci Technol 6:215–233.  https://doi.org/10.1081/MST-120005957CrossRefGoogle Scholar
  16. 16.
    Kuts V, Nikolaev S, Ivanov I (2016) A new method for chatter detection in milling. Part 1: description and approbation. Scientific Open Access Journal “Naukovedenie”, MoscowGoogle Scholar
  17. 17.
    Elsner JB, Tsonis AA (1996) Singular spectrum analysis: a new tool in time series analysis. Springer Science & Business Media, New YorkCrossRefGoogle Scholar
  18. 18.
    Kiselev I, Voronov S (2014) Methodic of rational cutting conditions determination for 3-D shaped detail milling based on the process numerical simulation. In: ASME proceedings of 10th international conference on multibody systems, nonlinear dynamics and control, American Society of Mechanical Engineers.  https://doi.org/10.1115/detc2014-34894

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bauman Moscow State Technical UniversityMoscowRussia
  2. 2.Skolkovo Institute of Science and TechnologyMoscowRussia

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