Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites

被引：25

作者：

Tofigh, Ali Asghar ^{[1
]}

Rahimipour, Mohammad Reza ^{[1
]}

Shabani, Mohsen Ostad ^{[1
]}

Alizadeh, Mehdi ^{[1
]}

Heydari, Fatemeh ^{[1
]}

Mazahery, Ali ^{[1
]}

Razavi, Mansour ^{[1
]}

机构：

[1] MERC, Tehran, Iran

来源：

JOURNAL OF MANUFACTURING PROCESSES | 2013年 / 15卷 / 04期

关键词：

Aluminum; Metal matrix; Nano composites; Modeling; CAST A356 ALLOY; MECHANICAL-PROPERTIES; PARTICLE SWARM; MICROSTRUCTURE; PERFORMANCE; PREDICTION; STRENGTH; BEHAVIOR; GA;

D O I：

10.1016/j.jmapro.2013.08.004

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this research, an experimental study of reinforcing alumina nano-particles into the aluminum alloy matrix was implemented to verify the accuracy of modeling results obtained by feed forward neural networks. Artificial neural network combined with numerical technique were used to predict the various parameters of mechanical properties such as hardness, tensile and compressive yield stress, UTS and elongation percentage. Much experimentation were taken to discover a suitable number of hidden neurons, avoid detraction from the trainability and enable feed forward neural networks to solve more complex problems. The predictions were found to be consistent with experimental measurements. (C) 2013 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

引用

页码：518 / 523

页数：6

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