Neural Computing and Applications

, Volume 18, Issue 2, pp 135–140 | Cite as

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

  • D. Venkatesan
  • K. Kannan
  • R. Saravanan
Original Article


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.


Genetic algorithm Turning process Neural networks Machining parameters Turning operations 



Genetic algorithm


Artificial neural networks


Back propagation network


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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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