期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 48, 期 1, 页码 109-114出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.commatsci.2009.12.012
关键词
Undercooled liquid region; Glass forming alloys; Zr-Al-Ni-Cu; Artificial neural network
资金
- National Natural Science Foundation [50874045]
- Postdoctoral Science Foundation of China [20080431021, 200902472]
- Postdoctoral Science Foundation of Hunan Province [2008RS4021]
- Scientific Research Fund of the Hunan Provincial Education Department [06B038]
- Postdoctoral Science Foundation of Central South University
A computer model based on radial base function artificial neural network (RBFANN) was designed for the simulation and prediction of undercooled liquid region Delta T(x) of glass forming alloys. The model was trained using data from the published literature as well as own experimental data. The performance of RBFANN model is examined by the predicted and simulated results of the influence of kinds of alloys and elements, large and minor change of element content on the reduced glass transition temperature, and composition dependence of Delta T(x) for La-Al-Ni ternary alloy system. The results show that the RBFANN model is reliable and adequately. Moreover, a group of new Zr-Al-Ni-Cu bulk metallic glasses is designed by RBFANN model. Their predicted Delta T(x)s are in agreement with the corresponding experimental values. (C) 2009 Elsevier B.V. All rights reserved.
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