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Soft Computing and Industry pp 167–178Cite as

Prediction of Power Requirement in Turning using a GA-Fuzzy Approach

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Abstract

Empirical expressions had been developed by various investigators, in the past, to determine input-output relationships in turning. In the present work, power requirement in turning is expressed as a function of three inputs, namely cutting speed, feed and depth of cut and this process is modelled using a combined GA-Fuzzy approach. An optimized knowledge base (KB) of the fuzzy logic controller (FLC) which is the representative of the KB of a Lathe is obtained, off-line, using a genetic algorithm (GA). Thus, power requirement in turning can be predicted using the FLC for a particular set of input parameters before carrying out real experiment.

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References

  1. Sen G. C. and Bhattacharya A. (1965) Principle of Metal Cutting. New Central Book Agency, Calcutta, India

    Google Scholar 

  2. Choudhuri K. (2000) Multi-Objective optimization in Turning-using A Genetic Algorithm. M.Tech. thesis, Regional Engg. College, Durgapur, India

    Google Scholar 

  3. Petropoulos P. G. (1973) Optimal selection of machining rate variables by geometric programming. Int. J. Prod. Res. 11, 4, 305–314

    Article  Google Scholar 

  4. Iwata K., Murotsu Y, Iwatsubo T. and Fuhji S. (1972) A probabilistic approach to the determination of the optimum cutting conditions. Trans. ASME, J1. of Engg. for Industry. 94, 1099–1107

    Article  Google Scholar 

  5. Rao S. S. and Hati S. K. (1978) Computerized selection of optimum machining conditions for a job requiring multiple operations. Trans. ASME. 100, 356–362

    Article  Google Scholar 

  6. Ermer D. S. (1971) Optimization of the constrained machining economics problem by geometric programming. Trans. ASME, J1. of Engg. for Industry. 93, 1067–1072

    Article  Google Scholar 

  7. Goldberg D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass, USA

    MATH  Google Scholar 

  8. Kosko B. (1994) Neural Networks and Fuzzy Systems. Prentice-Hall, New Delhi, India

    Google Scholar 

  9. Karr C: (1991) Design of an adaptive fuzzy logic controller using a genetic algorithm. Proc. of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 450–457

    Google Scholar 

  10. Pratihar D. K. (2000) Path and Gait Generation of Legged Robots Using GA-Fuzzy Approach. Ph.D. thesis, IIT Kanpur, India

    Google Scholar 

  11. Pham D. T. and Karaboga D. (1991) Optimum design of fuzzy logic controllers using genetic algorithms. J1. Sys. Engg. 1, 114–118

    Google Scholar 

  12. Liska J. and Melsheimer S. S. (1994) Complete design of fuzzy logic systems using genetic algorithms. Proc. of 3rd IEEE Intl. Conf. on Fuzzy Systems, 1377–1382

    Google Scholar 

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Author information

Authors and Affiliations

  1. Robotics & AI Lab., Department of Mechanical Engineering, Regional Engineering College, Durgapur, Durgapu, 713 209, India

    Dilip Kumar Pratihar (Assistant Professor)

Authors
  1. Bikramjit Podder Under Graduate Students
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  2. Dilip Kumar Pratihar
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  3. Mohit Sehravat Under Graduate Students
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  4. Surojit Mondal Under Graduate Students
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  5. Ratan Joarder Under Graduate Students
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Editor information

Editors and Affiliations

  1. Enterprise Integration, Cranfield University, Cranfield, Bedford, MK43 0AL, UK

    Rajkumar Roy PhD

  2. Department of Pattern Recognition, Fraunhofer IPK-Berlin, Pascalstrasse 8-9, Berlin, 10587, Germany

    Mario Köppen Diplom-Phys

  3. Institute of Intelligent Electronics, Helsinki University of Technology, Otakaari 5 A, FIN-02150, Espoo, Finland

    Seppo Ovaska PhD

  4. Department of Information Engineering, Mie University, 1515 Kamihama-cho, Tsu, 514-8507, Japan

    Takeshi Furuhashi PhD

  5. Royal Institute of Technology, NADA/CV AP, 10044, Stockholm, Sweden

    Frank Hoffmann

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© 2002 Springer-Verlag London

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Podder, B., Pratihar, D.K., Sehravat, M., Mondal, S., Joarder, R. (2002). Prediction of Power Requirement in Turning using a GA-Fuzzy Approach. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_15

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  • DOI: https://doi.org/10.1007/978-1-4471-0123-9_15

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1101-6

  • Online ISBN: 978-1-4471-0123-9

  • eBook Packages: Springer Book Archive

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