Optimization of Al Matrix Reinforced with B4C Particles

被引:18
|
作者
Shabani, Mohsen Ostad [1 ]
Mazahery, Ali [1 ]
机构
[1] Islamic Azad Univ, Karaj Branch, Karaj, Iran
关键词
ARTIFICIAL NEURAL-NETWORK; TRIBOLOGICAL PROPERTIES; MECHANICAL-PROPERTIES; COMPETITIVE ALGORITHM; SWARM OPTIMIZATION; WEAR BEHAVIOR; ALLOY; COMPOSITES; SOLIDIFICATION; PREDICTION;
D O I
10.1007/s11837-012-0502-2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the current study, abrasive wear resistance and mechanical properties of A356 composite reinforced with B4C particulates were investigated. A center particle swarm optimization algorithm (CenterPSO) is proposed to predict the optimal process conditions in fabrication of aluminum matrix composites. Unlike other ordinary particles, the center particle has no explicit velocity and is set to the center of the swarm at every iteration. Other aspects of the center particle are the same as that of the ordinary particle, such as fitness evaluation and competition for the best particle of the swarm. Because the center of the swarm is a promising position, the center particle generally gets good fitness value. More importantly, due to frequent appearance as the best particle of swarm, it often attracts other particles and guides the search direction of the whole swarm.
引用
收藏
页码:272 / 277
页数:6
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