ORIGINAL ARTICLE A genetic algorithm-based artiﬁcial neural network model for the optimization of machining processes D. Venkatesan K. Kannan R. Saravanan Received: 5 September 2006 / Accepted: 20 December 2007 / Published online: 15 January 2008 Ó Springer-Verlag London Limited 2008 Abstract Artiﬁcial intelligent tools like genetic algo- rithm, 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 dependen- cies 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 1 Introduction Modeling methods can be used in several ﬁelds of pro- duction engineering, e.g., planning, optimization or controls. Difﬁculties in modeling the manufacturing pro- cesses are manifold. To name a few, the great number of different machining operations, multidimensional, nonlin- ear, stochastic nature of machining, partially understood relations between parameters, lack of reliable data are some stages, which one can overcome through modeling. One of the ways to overcome such difﬁculty is to implement fundamental models based on the principles of machining science. However, in spite of progress made in fundamental process modeling, accurate models are not yet available for manufacturing processes. Heuristic models are usually based on the thumb rules gained from experi- ence, and used for qualitative evaluation of decisions. Empirical models derived from experimental data still play a major role in manufacturing process modeling. Artiﬁcial neural networks (ANNs) can be used as operation models, because they can handle high level of nonlinearities, large number of parameters and missing information. Based on their inherent learning capabilities, ANNs can adapt themselves to changes in the production D. Venkatesan Department of Computer Science, Shanmugha Arts Science and Technology Research Academy (SASTRA), Thanjavur 613402, Tamilnadu, India e-mail: venkatgowri@yahoo.com K. Kannan Department of Mathematics, Shanmugha Arts Science and Technology Research Academy (SASTRA), Thanjavur 613402, Tamilnadu, India e-mail: kkannan@yahoo.com R. Saravanan (&) Department of Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore 641006, Tamilnadu, India e-mail: saradharani@hotmail.com 123 Neural Comput & Applic (2009) 18:135–140 DOI 10.1007/s00521-007-0166-y