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


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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.



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




H. J. Li et al., "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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