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论文类型：期刊论文

发表时间：2012-05-01

发表刊物：CHINA FOUNDRY

收录刊物：SCIE、Scopus

卷号：9

期号：2

页面范围：119-124

ISSN号：1672-6421

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

摘要：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.