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.

DOI: 10.1504/IJMR.2008.020900

International Journal of Manufacturing Research, 2008 Vol.3 No.4, pp.379 - 392

Published online: 23 Oct 2008 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article