期刊
ACTA MATERIALIA
卷 56, 期 5, 页码 1094-1105出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actamat.2007.10.059
关键词
hume-rothery's rules; artificial neural networks; solubility limit of metals; backpropagation networks; binary alloys
Hume-Rothery's breadth of knowledge combined with a quest for generality gave him insights into the reasons for solubility in metallic systems that have become known as Hume-Rothery's Rules. Presented with solubility details from similar sets of constitutional diagrams, can one expect artificial neural networks (ANN), which are blind to the underlying metals physics, to reveal similar or better correlations? The aim is to test whether it is feasible to predict solid solubility limits using ANN with the parameters that Hume-Rothery identified. The results indicate that the correlations expected by Hume-Rothery's Rules work best for a certain range of copper or silver alloy systems. The ANN can predict a value for solubility, which is a refinement on the original qualitative duties of Hume-Rothery's Rules. The best combination of input parameters can also be evaluated by ANN. (c) 2007 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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