GA-Fuzzy Approaches: Application to Modeling of Manufacturing Process

  • Arup Kumar Nandi


This chapter presents various techniques using the combination of fuzzy logic and genetic algorithm (GA) to construct model of a physical process including manufacturing process. First, an overview on the fundamentals of fuzzy logic and fuzzy inferences systems toward formulating a rule-based model (called fuzzy rule based model, FRBM) is presented. After that, the working principle of a GA is discussed and later, how GA can be combined with fuzzy logic to design the optimal knowledge base of FRBM of a process is presented. Results of few case studies of modeling various manufacturing processes using GA-fuzzy approaches conducted by the author are presented.


Genetic Algorithm Membership Function Fuzzy Rule Fuzzy Inference System Fuzzy Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Groover, M.: Automation, Production System, and Computer Integrated Manufacturing. Prentice-Hall Int’l., Upper Saddle River (2001)Google Scholar
  2. 2.
    Kosko, B.: Neural Network and Fuzzy Systems. Prentice-Hall, New Delhi (1994)Google Scholar
  3. 3.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)CrossRefzbMATHGoogle Scholar
  5. 5.
    Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets and Systems 28(1), 15–33 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Tsukamoto, Y.: Fuzzy information theory. Daigaku Kyoiku Pub. (2004)Google Scholar
  7. 7.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15(1), 116–132 (1985)CrossRefzbMATHGoogle Scholar
  8. 8.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  9. 9.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons Ltd., England (2001)zbMATHGoogle Scholar
  10. 10.
    Nandi, A.K., Pratihar, D.K.: Automatic Design of Fuzzy Logic Controller Using a Genetic Algorithm – to Predict Power Requirement and Surface finish in Grinding. Journal of Material Processing Technology 148(3), 288–300 (2004)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Nandi, A.K.: TSK-Type FLC using a combined LR and GA: surface roughness prediction in ultraprecision turning. Journal of Material Processing Technology 178(1-3), 200–210 (2006)CrossRefGoogle Scholar
  12. 12.
    Chandrasekaran, M., Muralidhar, M., Krishna, C.M., Dixit, U.S.: Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int. J. Advance Manufacturing Technology 46, 445–464 (2010)CrossRefGoogle Scholar
  13. 13.
    Nandi, A.K., Pratihar, D.K.: Design of a Genetic-Fuzzy System to Predict Surface finish and Power Requirement in Grinding. Fuzzy Sets and Systems 148(3), 87–504 (2004)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Nandi, A.K., Davim, J.P.: A Study of drilling performances with Minimum Quantity of Lubricant using Fuzzy Logic Rules. Mechatronics 19(2), 218–232 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arup Kumar Nandi
    • 1
  1. 1.Central Mechanical Engineering Research Institute (CSIR-CMERI)DurgapurIndia

Personalised recommendations