Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks

Abstract:

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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 and 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

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$35.00

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