Paper Title:
Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks
  Abstract

In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation (BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been deduced by using gradient descent algorithms. A BP neural network has been established between the heat treatment technique and the hardness. The results indicated that the predicted results are closed to the test results. The weakness that the nonlinear and time variation relationship between heat treatment and the hardness could be approached more accurately, effectively by using single-factor-experiment method has been overcome. Hence providing a effective, economical,rapid way for the heat treatment optimization of nonferrous metals and ferrous metal.

  Info
Periodical
Advanced Materials Research (Volumes 217-218)
Edited by
Zhou Mark
Pages
1458-1461
DOI
10.4028/www.scientific.net/AMR.217-218.1458
Citation
J.P. Ren, R.G. Song, "Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks", Advanced Materials Research, Vols. 217-218, pp. 1458-1461, 2011
Online since
March 2011
Authors
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Price
$32.00
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