Revisiting Hume-Rothery's Rules with artificial neural networks
Revisiting Hume-Rothery's Rules with artificial neural networks
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.
hume-rothery's rules, artificial neural networks, solubility limit of metals, backpropagation networks, binary alloys
1094-1105
Zhang, Y.M.
fcc93306-15b2-4fba-963b-579bba27bde3
Yang, S.
e0018adf-8123-4a54-b8dd-306c10ca48f1
Evans, J.R.G.
6f6c8a4c-24ac-4144-a555-51438e4d40e0
March 2008
Zhang, Y.M.
fcc93306-15b2-4fba-963b-579bba27bde3
Yang, S.
e0018adf-8123-4a54-b8dd-306c10ca48f1
Evans, J.R.G.
6f6c8a4c-24ac-4144-a555-51438e4d40e0
Zhang, Y.M., Yang, S. and Evans, J.R.G.
(2008)
Revisiting Hume-Rothery's Rules with artificial neural networks.
Acta Materialia, 56 (5), .
(doi:10.1016/j.actamat.2007.10.059).
Abstract
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.
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Published date: March 2008
Keywords:
hume-rothery's rules, artificial neural networks, solubility limit of metals, backpropagation networks, binary alloys
Organisations:
Engineering Mats & Surface Engineerg Gp
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Local EPrints ID: 165039
URI: http://eprints.soton.ac.uk/id/eprint/165039
ISSN: 1359-6454
PURE UUID: fafcef38-8568-4d51-a755-28e1ebb9c972
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Date deposited: 07 Oct 2010 11:03
Last modified: 14 Mar 2024 02:09
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Author:
Y.M. Zhang
Author:
J.R.G. Evans
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