Skip to main content
SpringerLink
Log in

Tool wear monitoring using naïve Bayes classifiers

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

Abstract

A naïve Bayes classifier method for tool condition monitoring is described. End-milling tests were performed at different spindle speeds and the cutting force was measured using a table-mounted dynamometer. The effect of tool wear on force features in the time and frequency domains was evaluated and used for training the classifier. The amount of tool wear was predicted using the naïve Bayes classifier method. Two cases are presented. First, the tool wear is divided into discrete states based on the amount of flank wear and the probability of the tool wear being in any state is updated using force data. Second, a continuous case is considered and the probability density function of the tool flank wear width is updated. The results are discussed.

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

Similar content being viewed by others

References

  1. Konstantinos S, Athanasios K (2014) Reliability assessment of cutting tool life based on surrogate approximation methods. Int J Adv Manuf Technol 71(5–8):1197–1208

  2. Yeo S, Khoo L, Neo S (2000) Tool condition monitoring using reflectance of chip surface and neural network. J Intell Manuf 11:507–514

    Article  Google Scholar 

  3. Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comp Ind 34:55–72

    Article  Google Scholar 

  4. Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26(7–8):693–710

    Article  Google Scholar 

  5. Prickett PW, Johns C (1999) An overview of approaches to end milling tool monitoring. Int J Mach Tools Manuf 39:105–122

    Article  Google Scholar 

  6. Dimla E Snr (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:10735–11098

  7. Burke LI, Rangwala S (1991) Tool condition monitoring in metal cutting: a neural network approach. J Intell Manuf 2(5):269–280

    Article  Google Scholar 

  8. Sick B (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks—a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546

    Article  Google Scholar 

  9. Dornfeld DA (1990) Neural network sensor fusion for tool condition monitoring. Ann CIRP 39:101–105

    Article  Google Scholar 

  10. Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21:466–479

    Article  Google Scholar 

  11. Jemielniak K, Leszek K, Pawel W (1998) Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network. J Intell Manuf 9(5):447–455

    Article  Google Scholar 

  12. Aliustaoglu C, Ertunc HM, Ocak H (2009) Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech Syst Signal Process 23(2):539–546

    Article  Google Scholar 

  13. Li X, Yuan Z (1998) Tool wear monitoring with wavelet packet transform—fuzzy clustering method. Wear 219(2):145–154

    Article  Google Scholar 

  14. Chen JC, Susanto V (2003) Fuzzy logic based in-process tool-wear monitoring system in face milling operations. Int J Adv Manuf Technol 21(3):186–192

    Article  Google Scholar 

  15. Kuo RJ, Cohen PH (1998) Intelligent tool wear estimation system through artificial neural networks and fuzzy modeling. Artif Intell Eng 12(3):229–242

    Article  Google Scholar 

  16. Li AU, Elbestawi MA (1996) Fuzzy clustering for automated tool condition monitoring in machining. J Mech Syst Signal Proc 10(5):533–550

    Article  Google Scholar 

  17. Li X, Yao Y, Yuan Z (1997) On-line tool condition monitoring system with wavelet fuzzy neural network. J Intell Manuf 8(4):271–276

    Article  Google Scholar 

  18. Chen JC (1996) A fuzzy nets tool breakage detection system for end milling operations. J Adv Manuf Technol 12:153–164

    Article  Google Scholar 

  19. Dey S, Stori JA (2005) A Bayesian network approach to root cause diagnosis of process variations. Int J Mach Tools Manuf 45(1):75–91

    Article  Google Scholar 

  20. Atlas L, Ostendod M, Bernard GD (2000) Hidden Markov Models for monitoring tool wear. IEEE Int Conf Acoust Speech Signal Proc 6:3887–3890

    Google Scholar 

  21. Ertunc HM, Loparo KA (2001) A decision fusion algorithm for tool wear condition monitoring in drilling. Int J Mach Tools Manuf 41(9):1347–1362

    Article  Google Scholar 

  22. Ertunc HM, Loparo KA, Ocak H (2001) Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs). Int J Mach Tools Manuf 41:1363–1384

    Article  Google Scholar 

  23. Vallejo AG, Nolazco-Flores JA, Morales-Menéndez R, Sucar LE, Rodríguez CA (2005) Tool-Wear Monitoring Based on Continuous Hidden Markov Models, Progress in Pattern recognition. Image Anal Appl Lect Comp Sci 3773:880–890

    Google Scholar 

  24. Constantinides N, Benett S (1987) An investigation of methods for on-line estimation of tool wear. Int J Mach Tools Manuf 27(2):225–237

    Article  Google Scholar 

  25. Domingos P, Pazzani M (1997) Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Mach Learn 29:103–130

    Article  MATH  Google Scholar 

  26. Duda RO, Hart PE, Stork DG (2012) Pattern classification. John Wiley & Sons

  27. Zhang H (2004) The Optimality of Naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference. AAAI Press, Miami Beach

    Google Scholar 

  28. Geiger D, Heckerman D (1997) A characterization of the Dirichlet distribution through global and local independence. Ann Stat 25:1344–1369

    Article  MATH  MathSciNet  Google Scholar 

  29. Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis. Chapman and Hall CRC, Boca Raton

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Jaydeep Karandikar

  2. Advanced Manufacturing Research Centre with Boeing, University of Sheffield, Rotherham, UK

    Tom McLeay & Sam Turner

  3. Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, Charlotte, NC, USA

    Tony Schmitz

Authors
  1. Jaydeep Karandikar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tom McLeay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sam Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tony Schmitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaydeep Karandikar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karandikar, J., McLeay, T., Turner, S. et al. Tool wear monitoring using naïve Bayes classifiers. Int J Adv Manuf Technol 77, 1613–1626 (2015). https://doi.org/10.1007/s00170-014-6560-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6560-6

Keywords

Search

Navigation