Computational Methods and Optimization in Machining of Metal Matrix Composites



This chapter deals with the importance of mathematical modeling and need for optimizing the process. Further, case studies involving the various modeling and optimization techniques applied to machining of metal matrix composites are also discussed.


Particle Swarm Optimization Artificial Neural Network Feed Rate Response Surface Methodology Metal Matrix Composite 
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The authors would like to thank Elsevier and SAGE publications for granting permission for re-use of the published materials.


  1. 1.
    Montgomery DC (2004) Design and analysis of experiments. Wiley, New YorkGoogle Scholar
  2. 2.
    Myers RH, Montgomery DC, Anderson-Cook CM (2009) Response surface methodology. Wiley, New JerseyMATHGoogle Scholar
  3. 3.
    Gaitonde VN, Karnik SR, Davim JP (2009) Design of experiments. In: Ozel T, Davim J (eds) Intelligent machining: modeling and optimization of the machining processes and systems. Wiley, USA, pp 215–243Google Scholar
  4. 4.
    Phadke MS (1989) Quality engineering using robust design. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  5. 5.
    Satishkumar S, Asokan P, Kumanan S (2006) Optimization of depth of cut in multi-pass turning using nontraditional optimization techniques. Int J Adv Manuf Technol 29:230–238CrossRefGoogle Scholar
  6. 6.
    Gaitonde VN, Karnik SR, Davim JP (2009) Some studies in metal matrix composites machining using response surface methodology. J Reinforc Plast Compos 28(20):2445–2457CrossRefGoogle Scholar
  7. 7.
    Schalkoff GB (1997) Artificial neural network. McGraw-Hill, SingaporeGoogle Scholar
  8. 8.
    Muthukrishnan N, Davim JP (2009) Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis. J Mater Process Technol 209:225–232CrossRefGoogle Scholar
  9. 9.
    Ross PJ (1996) Taguchi techniques for quality engineering. McGraw-Hill, SingaporeGoogle Scholar
  10. 10.
    Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, New YorkMATHGoogle Scholar
  11. 11.
    Deb K (1995) Optimization for engineering design: algorithms and examples. Prentice-Hall, New YorkGoogle Scholar
  12. 12.
    Dorigo M (1996) The ant system: optimization by a colony of cooperating agent. IEEE Trans Syst Man Cybern Part B 26(1):1–13CrossRefGoogle Scholar
  13. 13.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. University of Western Australia, Perth, Western Australia, pp 1942–1948Google Scholar
  14. 14.
    Antonio CAC, Davim JP (2002) Optimal cutting conditions in turning of particulate metal matrix composites based on experiment and a genetic search model. Composites Part A 33:213–219CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • V. N. Gaitonde
    • 1
  • S. R. Karnik
    • 2
  • J. Paulo Davim
    • 3
  1. 1.Department of Industrial and Production EngineeringB. V. B. College of Engineering and TechnologyHubliIndia
  2. 2.Department of Electrical and Electronics EngineeringB. V. B. College of Engineering and TechnologyHubliIndia
  3. 3.Department of Mechanical EngineeringUniversity of AveiroAveiroPortugal

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