Paper Title:
Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy
  Abstract

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

  Info
Periodical
Chapter
Chapter 2: Turning, Milling and Drilling
Edited by
Taghi Tawakoli
Pages
418-423
DOI
10.4028/www.scientific.net/AMR.325.418
Citation
S. Zhang, J. F. Li, "Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy", Advanced Materials Research, Vol. 325, pp. 418-423, 2011
Online since
August 2011
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Price
$32.00
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