Paper Title:
Prediction of Surface Roughness in High Speed Milling Process Using the Artificial Neural Networks
  Abstract

The objective of this research was to apply the artificial neural network algorithm to predict the surface roughness in high speed milling operation. Tool length, feed rate, spindle speed, cutting path interval and run-out were used as five input neurons; and artificial neural networks model based on back-propagation algorithm was developed to predict the output neuron-surface roughness. A series of experiments was performed, and the results were estimated. The experimental results showed that the applied artificial neural network surface roughness prediction gave good accuracy in predicting the surface roughness under a variety of combinations of cutting conditions.

  Info
Periodical
Key Engineering Materials (Volumes 364-366)
Edited by
Guo Fan JIN, Wing Bun LEE, Chi Fai CHEUNG and Suet TO
Pages
713-718
DOI
10.4028/www.scientific.net/KEM.364-366.713
Citation
D. W. Kim, Y. J. Shin, K. T. Park, E. S. Lee, J. H. Lee, M. W. Cho, "Prediction of Surface Roughness in High Speed Milling Process Using the Artificial Neural Networks", Key Engineering Materials, Vols. 364-366, pp. 713-718, 2008
Online since
December 2007
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Price
$32.00
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