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Surface roughness prediction in machining using Computational Intelligence

Surface roughness prediction in machining using Computational Intelligence 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). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Manufacturing Research Inderscience Publishers

Surface roughness prediction in machining using Computational Intelligence

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1750-0591
eISSN
1750-0605
DOI
10.1504/IJMR.2008.0209
Publisher site
See Article on Publisher Site

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).

Journal

International Journal of Manufacturing Research – Inderscience Publishers

Published: Jan 1, 2008

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