Neural Computing and Applications

, Volume 18, Issue 2, pp 135–140

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Original Article

Abstract

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.

Keywords

Genetic algorithm Turning process Neural networks Machining parameters Turning operations 

Abbreviations

GA

Genetic algorithm

ANN

Artificial neural networks

BPN

Back propagation network

References

  1. 1.
    Monostori L, Viharos Zs J, Markos S (2000) Satisfying various requirements in different levels and stages of machining using one general ANN-based process model. J Mater Process Technol 107:228–235CrossRefGoogle Scholar
  2. 2.
    Knapp GM, Hsu-Pin W (1992) Acquiring, storing and utilizing process planning knowledge using neural networks. J Intell Manuf 3(5):333–344CrossRefGoogle Scholar
  3. 3.
    Dini G (1995) A neural approach to the automated selection of tools in turning. In: Proceedings of the second AITEM conference, Padova, Italy, 18–20 September 1995. pp. 1–10Google Scholar
  4. 4.
    Choi GH, Lee KD, Chang N, Kim SG (1994) Optimization of the process parameters of injection modeling with neural network application in process simulation environment. CIRP Ann 43(1):449–452CrossRefGoogle Scholar
  5. 5.
    Hatamura Y, Nagao T, Kato KI, Taguchi S, Okumura T, Nakagawa G, Sugishita H (1993) Development of an intelligent machining center incorporating active compensation for thermal distortion. CIRP Ann 42(1):549–552CrossRefGoogle Scholar
  6. 6.
    Monostori L (1993) A step towards intelligent manufacturing: modeling and monitoring of manufacturing process through artificial neural networks. CIRP Ann 42(1):485–488CrossRefGoogle Scholar
  7. 7.
    Liao TW, Chen LJ (1994) A neural network approach for grinding processes: modeling and optimization. Int J Mach Tools Manuf 34(7):919–937CrossRefMathSciNetGoogle Scholar
  8. 8.
    Viharos ZsJ, Monostori L (1999) Automatic input–output configuration and generation of ANN-based process models and its application in machining. In: Imam I, Kodratoff Y, El-Dessouki A, Ali M (eds) Proceedings of the XIIth international conference on industrial and engineering applications of artificial intelligence and expert systems, IEA/AIE-99, Keiro, Egypt, 1999. Springer, New York, pp 659–668Google Scholar
  9. 9.
    Montana DJ Neural network weight selection using genetic algorithms. http://www.vishnu.bbn.com/papers/hybrid.com
  10. 10.
    Seiffert U (2001) Multiple layer perceptron training using genetic algorithms. In: Proceedings of the 9th European symposium on artificial neural networks (ESANN 2001), Bruges, Belgium, 25–27 April 2001. D-Facto, Evere, Belgium, pp 25–27Google Scholar
  11. 11.
    Abu-Al-Nadi DI Training feedforward neural networks with a modified genetic algorithm. http://www.ines-conf.org/ines-conf/2004list.htm

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.Department of Computer ScienceShanmugha Arts Science and Technology Research Academy (SASTRA)ThanjavurIndia
  2. 2.Department of MathematicsShanmugha Arts Science and Technology Research Academy (SASTRA)ThanjavurIndia
  3. 3.Department of Mechatronics EngineeringKumaraguru College of TechnologyCoimbatoreIndia

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