A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

Abstract:

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A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

Info:

Periodical:

Materials Science Forum (Volumes 488-489)

Edited by:

W.Ke, E.H.Han, Y.F.Han, K.Kainer and A.A.Luo

Pages:

793-796

DOI:

10.4028/www.scientific.net/MSF.488-489.793

Citation:

H. D. Liu et al., "A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network", Materials Science Forum, Vols. 488-489, pp. 793-796, 2005

Online since:

July 2005

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Price:

$35.00

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