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Tool Condition Monitoring Using the TSK Fuzzy Approach Based on Subtractive Clustering Method

  • Qun Ren
  • Marek Balazinski
  • Luc Baron
  • Krzysztof Jemielniak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

This paper presents a tool condition monitoring approach using Takagi-Sugeno-Kang (TSK) fuzzy logic incorporating a subtractive cluste- ring method. The experimental results show its effectiveness and satisfactory comparisons with several other artificial intelligence methods.

Keywords

tool condition monitoring TSK fuzzy logic subtractive clustering 

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References

  1. 1.
    El Gomayel, J.I., Bregger, K.D.: On-line Tool. Trans. of ASME. J. of Eng. in Industry 108(1), 44–47 (1988)Google Scholar
  2. 2.
    Du, R., Elbestawi, M.A., Wu, S.M.: Automated monitoring of manufacturing processes, Part 1 and Part 2. J. of Eng. In Industry 117, 121–141 (1995)CrossRefGoogle Scholar
  3. 3.
    Balazinski, M., Bellerose, M., Czogala, E.: Application of fuzzy logic techniques to the selection of cutting parameters in machining processes. Fuzzy Sets and Systems 61, 301–317 (1994)Google Scholar
  4. 4.
    Balazinski, M., Czogala, E., Jemielniak, K., Leski, J.: Tool condition monitoring using artificial intelligence methods. Eng. App. of Art. Int. 15, 73–80 (2002)CrossRefGoogle Scholar
  5. 5.
    Jemielniak, K., Kwiatkowski, L., Wrzosek, P.: Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to neural network. J. of Int. Man. 9, 447–455 (1998)CrossRefGoogle Scholar
  6. 6.
    Balazinski, M., Jemielniak, K.: Tool conditions monitoring using fuzzy decision support system. In: VCIRP, AC 1998 Miedzeszyn, pp. 115–122 (1998)Google Scholar
  7. 7.
    Baron, L., Archiche, S., Balazinski, M.: Fuzzy decisions system knowledge base generation using a genetic algorithm. Int. J. of App. Reasoning 28(2-3), 125–148 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Li, X.L., Li, H.X., Guan, X.P., Du, R.: Fuzzy estimation of feed-cutting force from current measurement-a case study on intelligent tool wear condition monitoring. IEEE Trans. on Sys., Man and Cyb., Part C (Applications and Reviews) 34(4), 506–512 (2004)CrossRefGoogle Scholar
  9. 9.
    Achiche, S., Balazinski, M., Baron, L., Jemielniak, K.: Tool wear monitoring using genetically-generated fuzzy knowledge bases. Eng. App. of Art. Int. 15(3-4), 303–314 (2002)CrossRefGoogle Scholar
  10. 10.
    Chiu, S.L.: Fuzzy Model Identification Based on Cluster Estimation. J. on Int. Fuzzy Sys. 2, 267–278 (1994)MathSciNetGoogle Scholar
  11. 11.
    Zadeh, L.A.: Fuzzy Algorithm. Inf. and Contr. 12, 94–102 (1968)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Zadeh, L.A.: Outline of A New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Trans. on Sys., Man, and Cyb. 3, 28–44 (1973)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Zadeh, L.A.: Fuzzy Sets. Inf. and Cont. 8, 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modelling and Control. IEEE Trans. on Sys., Man, and Cyb. 15(1), 116–132 (1985)zbMATHGoogle Scholar
  15. 15.
    Sugeno, M., Kang, G.: Structure Identification of Fuzzy Model. Fuzzy Sets and Systems 28(1), 15–33 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Mamdani, E.H., Assilian, S.: Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proc. Inst. Elec. Eng. 121, 1585–1588 (1974)Google Scholar
  17. 17.
    Johansen, T., Foss, B.: Identification of Non-Linear System Structure and Parameters Using Regime Decomposition. Automatic 31(2), 321–326 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Füssel, D., Ballé, P., Isermann, R.: Closed Loop Fault Diagnosis Based on A Nonlinear Process Model and Automatic Fuzzy Rule Generation. In: 4th IFAC Sym. on Fault Det., Sup. and Safety for Tech.l Proc. (1997)Google Scholar
  19. 19.
    Wang, L.-X., Mendel, J.M.: Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. on Neural Networks 3, 807–813 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Qun Ren
    • 1
  • Marek Balazinski
    • 1
  • Luc Baron
    • 1
  • Krzysztof Jemielniak
    • 2
  1. 1.Mechanical Engineering DepartmentÉcole Polytechnique de MontréalMontréalCanada
  2. 2.Faculty of Production EngineeringWarsaw University of TechnologyWarsawPoland

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