Journal of Failure Analysis and Prevention

, Volume 17, Issue 5, pp 905–913 | Cite as

Prediction of Tool Wear in the Turning Process Using the Spectral Center of Gravity

  • Mohamed Khemissi Babouri
  • Nouredine Ouelaa
  • Mohamed Cherif Djamaa
  • Abderrazek Djebala
  • Nacer Hamzaoui
Technical Article---Peer-Reviewed


The recent increase in machining productivity is closely related to longer tool life and good surface quality. In the present study, an experimental technique is proposed to evaluate the performance of a cemented carbide inset during the machining of AISI D3 steel. The aim of this technique is to find a relationship between the vibratory state of the cutting tool and the corresponding wear during machining in order to detect the beginning of the transition period to excessive wear. A spectral indicator named spectral center of gravity, SCG, is proposed to highlight the three phases of tool wear using the spectra of the accelerations measured. Very promising results are obtained which can be used to underpin an industrial monitoring system capable of detecting the onset of transition to excessive wear and alerting the user of the end of the tool’s life. The purpose of this study is to review the vibration analysis techniques and to explore their contributions, advantages and drawbacks in monitoring of tool wear.


Vibration signatures AISI D3 steel Cutting tools SCG Wear Scalar indicators 

List of symbols


Cutting speed (m/min)


Feed rate (mm/rev)


Depth of cut (mm)


Root mean square


Flank wear (mm)


Arithmetic average of absolute roughness (µm)


Maximum height of the profile (µm)


Average maximum height of the profile (µm)


Spectral center of gravity


Overall level


Wavelet multi-resolution analysis


Sampling frequency


Value of the autospectrum at the sampling frequency


Rake angle (°)


Inclination angle (°)


Major cutting edge angle (°)



This work was completed in the Laboratory of Mechanics and Structures, University 8 May 1945, Guelma, Algeria. The authors would like to thank the Algerian Ministry of Higher Education and Scientific Research for granting financial support of the CNEPRU Research Project—LMS No.: J0301520130034 (University of 8 May 1945 Guelma).


  1. 1.
    D.E. Daimler, Sensor signals for tool-wear monitoring in metal cutting operation—a review of methods. Int. J. Mach. Tools Manuf 40, 1073–1098 (2000)CrossRefGoogle Scholar
  2. 2.
    M.K. Babouri, N. Ouelaa, A. Djebala, Experimental study of tool life transition and wear monitoring in turning operation using a hybrid method based on wavelet multi-resolution analysis and empirical mode decomposition. Int. J. Adv. Manuf. Technol. 82, 2017–2028 (2016)CrossRefGoogle Scholar
  3. 3.
    R. Suresh, S. Basavarajappa, G.L. Samuel, Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool. Measurement 45, 1872–1884 (2012)CrossRefGoogle Scholar
  4. 4.
    A.K. Sahoo, B. Sahoo, Experimental investigations on machinability aspects in finish hard turning of AISI 4340 steel using uncoated and multilayer coated carbide inserts. Measurement 45, 2153–2165 (2012)CrossRefGoogle Scholar
  5. 5.
    C. Xiaozhi, L. Beizhi, Acoustic emission method for tool condition monitoring based on wavelet analysis. Int. J. Adv. Manuf. Technol. 33, 968–976 (2007)CrossRefGoogle Scholar
  6. 6.
    K. Jemielniak, O. Otman, Tool failure detection based on analysis of acoustic emission signals. J. Mater. Process. Technol. 76, 192–197 (1998)CrossRefGoogle Scholar
  7. 7.
    L. Weighing, G. Xeiguo, T. Obikawa, T. Shirakashi, A method of recognizing tool-wear states based on a fast algorithm of wavelet transform. J. Mater. Process. Technol. 170, 374–380 (2005)CrossRefGoogle Scholar
  8. 8.
    C.M. Cemal, I. Yahya, Detecting tool breakage in turning AISI 1050 steel using coated and uncoated cutting tools. J. Mater. Process. Technol. 159, 191–198 (2005)CrossRefGoogle Scholar
  9. 9.
    I. Yahya, C.M. Cemal, Finite element analysis of cutting tools prior to fracture in hard turning operations. Mater. Des. 26, 105–112 (2005)CrossRefGoogle Scholar
  10. 10.
    J.M. Zhou, M. Andersson, J.E. Stähl, A system for monitoring cutting tool spontaneous failure based on stress estimation. J. Mater. Process. Technol. 48, 231–237 (1995)CrossRefGoogle Scholar
  11. 11.
    M.K. Babouri, N. Ouelaa, A. Djebala, Identification de l’évolution de l’usure d’un outil de tournage basée sur l’analyse des efforts de coupe et des vibrations. Revue Sci. Technol. Synthèse 24, 123–134 (2012)Google Scholar
  12. 12.
    M.K. Babouri, N. Ouelaa, A. Djebala, Temporal and frequential analysis of the tools wear evolution. Mechanika 20, 205–2012 (2014)CrossRefGoogle Scholar
  13. 13.
    W. Rmili, A. Ouahabi, R. Serra, M. Kious, Tool wear monitoring in turning processes using vibratory analysis. Int. J. Acoust. Vib. 14, 4–11 (2009)Google Scholar
  14. 14.
    M. Kious, M. Boudraa, A. Ouahabi, R. Serra, Influence of machining cycle of horizontal milling on the quality of cutting force measurement for the cutting tool wear monitoring. Prod. Eng. Res. Dev. 2, 443–449 (2008)CrossRefGoogle Scholar
  15. 15.
    Sandvik Coromant, Outils de coupe Sandvik Coromant, Tournage–Fraisage–Perçage–Alésage—Attachements (2009)Google Scholar
  16. 16.
    S. Khamel, N. Ouelaa, K. Bouacha, Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool. J. Mech. Sci. Technol. 26, 3605–3616 (2012)CrossRefGoogle Scholar
  17. 17.
    J. Krimphoff, S. Mcadams, S. Winsberg, Caractérisation du timbre des sons complexes. II. Analyses acoustiques et quantification psychophysique. J. Phys. IV(C5), 525–528 (1994)Google Scholar
  18. 18.
    M. Kenzari, Vibroacoustic diagnosis of gears defects: sound perception approach analysis. Master of Sciences Thesis, INSA of Lyon, France, 2009Google Scholar
  19. 19.
    R. Younes, N. Hamzaoui, N. Ouelaa, A. Djebala, A perceptual study of the evolution of gear defects. Appl. Acoust. 99, 60–67 (2015)CrossRefGoogle Scholar

Copyright information

© ASM International 2017

Authors and Affiliations

  • Mohamed Khemissi Babouri
    • 1
    • 2
  • Nouredine Ouelaa
    • 2
  • Mohamed Cherif Djamaa
    • 2
  • Abderrazek Djebala
    • 2
  • Nacer Hamzaoui
    • 3
  1. 1.Department of Mechanical Engineering and Productics (CMP), FGM & GPUniversity of Sciences and Technology Houari BoumedieneAlgiersAlgeria
  2. 2.Mechanics and Structures Laboratory (LMS)May 8th 1945 UniversityGuelmaAlgeria
  3. 3.Laboratory of Vibration-AcousticsINSA of LyonVilleurbanne CedexFrance

Personalised recommendations