Using genetic algorithm and artificial neural network analyses to design an Al-Si casting alloy of minimum porosity

被引：80

作者：

Anijdan, SHM

Bahrami, A

Hosseini, HRM

Shafyei, A

机构：

[1] Sharif Univ Technol, Dept Mat Sci & Engn, Tehran, Iran

[2] Isfahan Univ Technol, Dept Mat Engn, Esfahan, Iran

来源：

MATERIALS & DESIGN | 2006年 / 27卷 / 07期

关键词：

genetic algorithm (H); artificial neural network (H); porosity (E);

D O I：

10.1016/j.matdes.2004.11.027

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 (GA) has been developed to optimize effective parameters on porosity formation in Al-Si casting alloys. The ANN theory was used to correlate the chemical composition and cooling rate to the amount of porosity. The GA and ANN were incorporated to find the optimal conditions for achieving the minimum porosity percent. By comparing the predicted values with the experimental data - earlier deduced by Dash et al. - it is demonstrated that the combined GA-ANN model is a useful and efficient method to find the optimal conditions for casting of Al-Si alloys associated with the minimum porosity percent. (C) 2004 Elsevier Ltd. All rights reserved.

引用

页码：605 / 609

页数：5

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