Artificial neural network modeling of reduced glass transition temperature of glass forming alloys

A model based on radial base function artificial neural network (RBFANN) was designed for the simulation and prediction of reduced glass transition temperature T rg of glass forming alloys. Its performance is examined by the influences of different kinds of alloys and elements, large and minor chang...

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Bibliographic Details
Published in:Applied physics letters 2008-03, Vol.92 (11), p.111909-111909-3
Main Authors: Cai, An-hui, Xiong, Xiang, Liu, Yong, An, Wei-ke, Tan, Jing-ying
Format: Article
Language:English
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Summary:A model based on radial base function artificial neural network (RBFANN) was designed for the simulation and prediction of reduced glass transition temperature T rg of glass forming alloys. Its performance is examined by the influences of different kinds of alloys and elements, large and minor change of element content on the T rg , and composition dependence of T rg for La-Al-Ni ternary alloy system. Moreover, a group of Zr-Al-Ni-Cu bulk metallic glasses is designed by RBFANN. The values of T rg predicted by RBFANN agree well with the experimental values, indicating that the model is reliable and adequate.
ISSN:0003-6951
1077-3118
DOI:10.1063/1.2899633