Paper Title:
Aging Properties Prediction of the Lead Frame Cu-Cr-Sn-Zn Alloy via Neural Network
  Abstract

The aging process of lead frame Cu-Cr-Sn-Zn alloy has only been studied empirically by trial-and-error method so far. This paper builds up the prediction model of the aging properties via a supervised artificial neural network(ANN) to model the non-linear relationship between parameters of aging process with respect to hardness and electrical conductivity properties of the alloy. The improved model is developed by the Levenberg- Marquardt training algorithm. The predicted values of the ANN coincide with the tested data. So the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Sn-Zn alloy. The optimized processing parameters are available at 475 C ° -520 C ° aging for 2h-1h.

  Info
Periodical
Materials Science Forum (Volumes 475-479)
Main Theme
Edited by
Z.Y. Zhong, H. Saka, T.H. Kim, E.A. Holm, Y.F. Han and X.S. Xie
Pages
3331-3334
DOI
10.4028/www.scientific.net/MSF.475-479.3331
Citation
H. J. Li, J. H. Su, Q. M. Dong, P. Liu, F. Z. Ren, "Aging Properties Prediction of the Lead Frame Cu-Cr-Sn-Zn Alloy via Neural Network", Materials Science Forum, Vols. 475-479, pp. 3331-3334, 2005
Online since
January 2005
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$32.00
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