Artificial neural network modeling for undercooled liquid region of glass forming alloys

被引：28

作者：

Cai, An-hui ^{[1
,2
]}

Xiong, Xiang ^{[2
]}

Liu, Yong ^{[2
]}

An, Wei-ke ^{[1
]}

Tan, Jing-ying ^{[1
]}

Luo, Yun ^{[1
]}

机构：

[1] Hunan Inst Sci & Technol, Dept Mech & Elect Engn, Yueyang 414000, Peoples R China

[2] Cent S Univ, State Key Lab Powder Met, Changsha 410083, Hunan, Peoples R China

来源：

COMPUTATIONAL MATERIALS SCIENCE | 2010年 / 48卷 / 01期

关键词：

Undercooled liquid region; Glass forming alloys; Zr-Al-Ni-Cu; Artificial neural network; BULK METALLIC GLASSES; SOFT-MAGNETIC PROPERTIES; AMORPHOUS-ALLOYS; THERMODYNAMIC PROPERTIES; ABILITY; TRANSITION; PARAMETERS; KINETICS;

D O I：

10.1016/j.commatsci.2009.12.012

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：109 / 114

页数：6

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