Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms

被引：35

作者：

Bhattacharya, Baidurya ^{[2
]}

Kumar, G. R. Dinesh ^{[1
]}

Agarwal, Akash ^{[1
]}

Erkoc, Sakir ^{[3
]}

Singh, Arunima ^{[1
]}

Chakraborti, Nirupam ^{[1
]}

机构：

[1] Indian Inst Technol, Dept Met & Mat Engn, Kharagpur 721302, W Bengal, India

[2] Indian Inst Technol, Dept Civil Engn, Kharagpur 721302, W Bengal, India

[3] Middle E Tech Univ, Dept Phys, TR-06531 Ankara, Turkey

来源：

COMPUTATIONAL MATERIALS SCIENCE | 2009年 / 46卷 / 04期

关键词：

Fe-Zn system; Hot-dip galvanizing; Molecular dynamics; Multi-objective optimization; Artificial neural networks; Genetic algorithms; MATERIALS SCIENCE; MATERIALS DESIGN; IDENTIFICATION; OPTIMIZATION; SIMULATIONS; NETWORKS; EXCHANGE; FRACTURE;

D O I：

10.1016/j.commatsci.2009.04.023

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Failure behavior of Zn coated Fe is simulated through molecular dynamics (MD) and the energy absorbed at the onset of failure along with the corresponding strain of the Zn lattice are computed for different levels of applied shear rate. temperature and thickness. Data-driven models are constructed by feeding the MD results to an evolutionary neural network. The outputs of these neural networks are utilized to carry out a multi-objective optimization through genetic algorithms, where the best possible tradeoffs between two conflicting requirements, minimum deformation and maximum energy absorption at the onset of failure, are determined by constructing a Pareto frontier. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：821 / 827

页数：7

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