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Indexed by:期刊论文

Date of Publication:2012-05-01

Journal:CHINA FOUNDRY

Included Journals:SCIE、Scopus

Volume:9

Issue:2

Page Number:119-124

ISSN No.:1672-6421

Key Words:artificial neural networks; Mg-Li-Al alloys; BP algorithm; particle swarm optimization; mechanical properties

Abstract:Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research. This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-Al alloys. Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-Al alloys, a momentum back-propagation (BP) neural network with a single hidden layer was established. Particle swarm optimization (PSO) was applied to optimize the BP model. In the neural network, the input variables were the contents of Mg, Li and Al, and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-Al alloy's composition and its mechanical properties. It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model. The prediction of the influence of Al addition on the mechanical properties of as-cast Mg-Li-Al alloys is consistent with the related research results.