Using GA-ANN algorithm to optimize soft magnetic properties of nanocrystalline mechanically alloyed Fe-Si powders

被引：32

作者：

Yazdanmehr, M. ^{[1
]}

Anijdan, S. H. Mousavi ^{[2
]}

Bahrami, A. ^{[3
]}

机构：

[1] Isfahan Univ, Dept Phys, Esfahan, Iran

[2] McGill Univ, Min Met & Mat Engn Dept, Montreal, PQ H3A 2B2, Canada

[3] Delft Univ Technol, Dept Mat Sci & Engn, NL-2628 CD Delft, Netherlands

来源：

COMPUTATIONAL MATERIALS SCIENCE | 2009年 / 44卷 / 04期

关键词：

Fe-Si powders; Artificial neural network; Genetic algorithm; Coercivity; Nanocrystalline; PREDICTION; PARAMETERS;

D O I：

10.1016/j.commatsci.2008.08.003

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this investigation a theoretical model based on artificial neural network (ANN) and genetic algorithm (CA) has been developed to optimize the magnetic softness in nanocrystalline Fe-Si powders prepared by mechanical alloying (MA). The ANN model was used to correlate the milling time, chemical composition, milling speed, and ball to powders ratio (BPR) to coercivity and crystallite size of nanocrystalline Fe-Si powders. The GA-ANN combined algorithm was incorporated to find the optimal conditions for achieving the minimum coercivity. By comparing the predicted values with the experimental data it is demonstrated that the combined CA-ANN algorithm is a useful, efficient and strong method to find the optimal milling conditions and chemical composition for producing nanocrystalline Fe-Si powders with minimum coercivity. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：1218 / 1221

页数：4

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