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Prediction of tool chatter and metal removal rate in turning operation on lathe using a new merged technique

Technical Paper
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Abstract

Tool chatter is an unstable phenomenon resulting in inaccurate machining and detritions of cutting tool. In this study, a combination of statistical approach and signal pre-processing technique has been adopted to explore the mechanism of tool chatter in turning operation. The effects of cutting parameters: depth of cut (d), feed (f) and spindle speed (N) on chatter have been investigated based on response surface model (RSM). Experimentally acquired raw chatter signals are pre-processed using wavelet transforms in order to remove the ambient noise contents. Further, to examine the influence of aforesaid cutting parameters on chatter severity, a new parameter, called chatter index (CI), has been evaluated. Furthermore, RSM has been adopted to develop quadratic and cubic mathematical models of CI and metal removal rate. Moreover, analysis of variance has been performed to check the statistical significance and combined effect of control parameters on machined output. More experiments have been conducted to validate the developed model. correlation between the predicted and experimental results validates the developed technique of ascertaining the tool chatter severity and metal removal rate.

Keywords

Wavelet denoising Chatter MRR RSM 

Notes

Acknowledgements

The authors gratefully acknowledge the Mechanical Engineering Department, IIT Indore, India for their help in conducting experiments.

References

  1. 1.
    Quintana G, Ciurana J (2011) Chatter in machining process: a review. Int J Mach Tool Manuf 51:363–376CrossRefGoogle Scholar
  2. 2.
    Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 61:27–47CrossRefGoogle Scholar
  3. 3.
    Sun H, Zhang X, Wang J (2016) Online machining chatter forecast based on improved local mean decomposition. Int J Adv Manuf Technol 84(5–8):1045–1056Google Scholar
  4. 4.
    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
  5. 5.
    Toh CK (2004) Vibration analysis in high speed rough and finish milling hardened steel. J Sound Vib 278(1):101–115CrossRefGoogle Scholar
  6. 6.
    Soliman E, Ismail F (1997) Chatter detection by monitoring spindle drive current. Int J Adv Manuf Technol 13(1):27–34CrossRefGoogle Scholar
  7. 7.
    Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4):672–693CrossRefGoogle Scholar
  8. 8.
    Albizuri J et al (2007) An active system of reduction of vibrations in a centerless grinding machine using piezoelectric actuators. Int J Mach Tools Manuf 47(10):1607–1614CrossRefGoogle Scholar
  9. 9.
    Dohner JL et al (2004) Mitigation of chatter instabilities in milling by active structural control. J Sound Vib 269(1):197–211CrossRefGoogle Scholar
  10. 10.
    Chen M, Knospe CR (2007) Control approaches to the suppression of machining chatter using active magnetic bearings. IEEE Trans Control Syst Technol 15(2):220–232CrossRefGoogle Scholar
  11. 11.
    Ma C, Ma J, Shamoto E, Moriwaki T (2011) Analysis of regenerative chatter suppression with adding the ultrasonic elliptical vibration on the cutting tool. Precis Eng 35(2):329–338CrossRefGoogle Scholar
  12. 12.
    Wu D, Chen K (2010) Chatter suppression in fast tool servo-assisted turning by spindle speed variation. Int J Mach Tools Manuf 50(12):1038–1047CrossRefGoogle Scholar
  13. 13.
    Hajikolaei KH et al (2010) Spindle speed variation and adaptive force regulation to suppress regenerative chatter in the turning process. J Manuf Process 12(2):106–115CrossRefGoogle Scholar
  14. 14.
    Clancy BE, Shin YC (2002) A comprehensive chatter prediction model for face turning operation including tool wear effect. Int J Mach Tools Manuf 42:1035–1044CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Altintas Y, Weck M (2004) Chatter stability of metal cutting and grinding. CIRP Ann Manuf Technol 53:619–642CrossRefGoogle Scholar
  17. 17.
    Bayly PV et al (2001) Theory of torsional chatter in twist drills: model, stability analysis and composition to test. J Manuf Sci Eng 123:552–561CrossRefGoogle Scholar
  18. 18.
    Sastry S, Kapoor SG, DeVor RE (2002) Floquet theory based approach for stability analysis of the variable speed face-milling process. J Manuf Sci Eng 124:10–17CrossRefGoogle Scholar
  19. 19.
    Tansel IN et al (2006) Transformations in machining. Part 2. Evaluation of machining quality and detection of chatter in turning by using s-transformation. Int J Mach Tools Manuf 46(1):43–50CrossRefGoogle Scholar
  20. 20.
    Taylor CM, Turner S, Sims ND (2010) Chatter, process damping, and chip segmentation in turning: a signal processing approach. J Sound Vib 329:4922–4935CrossRefGoogle Scholar
  21. 21.
    Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210:713–719CrossRefGoogle Scholar
  22. 22.
    Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Robot Comput Integr Manuf 25:989–998CrossRefGoogle Scholar
  23. 23.
    Hashmi KH et al (2016) Optimization of process parameters for high speed machining of Ti-6Al-4V using response surface methodology. Int J Adv Manuf Technol 85(5–8):1847–1856CrossRefGoogle Scholar
  24. 24.
    Sarıkaya M, Gullu A (2014) Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J Clean Prod 65:604–616CrossRefGoogle Scholar
  25. 25.
    Singh B, Nanda BK (2012) Investigation into the effect of surface roughness on the damping of tack-welded structures using response surface methodology approach. J Vib Control 19:547–559CrossRefGoogle Scholar
  26. 26.
    Singh B, Nanda BK (2012) Slip damping mechanism in welded structures using response surface methodology. Exp Mech 52:771–791CrossRefGoogle Scholar
  27. 27.
    Chabbi A et al (2017) Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability function. Measurement 95:99–115CrossRefGoogle Scholar
  28. 28.
    Bhushan RK (2013) Multiresponse optimization of Al Alloy-SiC composite machining parameters for minimum tool wear and maximum metal removal rate. J Manuf Sci Eng 135(2):021013CrossRefGoogle Scholar
  29. 29.
    Zhang L, Wang X, Liu S (2012) Analysis of dynamic stability in a turning process based on a 2-DoFs model with overlap factor. J Mech Sci Technol 26:1891–1899CrossRefGoogle Scholar
  30. 30.
    Tlusty J (2000) Manufacturing processes and equipment. Prentice Hall, New JerseyGoogle Scholar
  31. 31.
    Wu Y, Du R (1996) Feature extraction and assessment using wavelet packets for monitoring of machining processes. Mech Syst Signal Process 10(1):29–53CrossRefGoogle Scholar
  32. 32.
    Debnath L (2003) Wavelets and signal processing. Birkhauser, Boston.  https://doi.org/10.1007/978-1-4612-0025-3. ISBN 9780817642358CrossRefMATHGoogle Scholar
  33. 33.
    Montgomery DC (2009) Design and analysis of experiments. Wiley, New YorkGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentJaypee University of Engineering and TechnologyGunaIndia

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