Title: Surface roughness prediction in machining using Computational Intelligence
Authors: B. Samanta, C. Nataraj
Addresses: Department of Mechanical Engineering, Villanova University, Villanova, PA 19085, USA. ' Department of Mechanical Engineering, Villanova University, Villanova, PA 19085, USA
Abstract: A study is presented to model surface roughness in turning using Genetic Programming (GP). The machining parameters, namely, the spindle speed, feed rate, depth of cut and the workpiece tool vibration amplitudes in three orthogonal directions have been used as inputs to model the workpiece surface roughness. The input parameters and the corresponding functional relationship are automatically selected using GP and maximising the modelling accuracy. The effects of different GP parameters on the prediction accuracy and training time are studied. The results of the GP-based approach are compared with other Computational Intelligence (CI) techniques like Artificial Neural Networks (ANN).
Keywords: ANNs; artificial neural networks; genetic programming; feature selection; intelligent manufacturing systems; IMS; surface roughness modelling; spindle speed; feed rate; depth of cut; workpiece tool vibration; computational intelligence.
International Journal of Manufacturing Research, 2008 Vol.3 No.4, pp.379 - 392
Published online: 23 Oct 2008 *
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