A new method in prediction of TCP phases formation in superalloys

被引：46

作者：

Anijdan, SHM ^{[1
]}

Bahrami, A ^{[1
]}

机构：

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

来源：

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING | 2005年 / 396卷 / 1-2期

关键词：

artificial neural network; superalloys; TCP phases formation; electron-hole number;

D O I：

10.1016/j.msea.2005.01.012

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

The purpose of this investigation is to develop a model for prediction of topologically closed-packed (TCP) phases formation in superalloys. In this study, artificial neural networks (ANN), using several different network architectures, were used to investigate the complex relationships between TCP phases and chemical composition of superalloys. In order to develop an optimum ANN structure, more than 200 experimental data were used to train and test the neural network. The results of this investigation shows that a multilayer perceptron (MLP) form of the neural networks with one hidden layer and 10 nodes in the hidden layer has the lowest mean absolute error (MAE) and can be accurately used to predict the electron-hole number (N-v) and TCP phases formation in superalloys. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：138 / 142

页数：5

共 15 条

[1]

ANIJDAN SHM, 2004, P 6 S IR STEEL BAFGH, P305

[2]

BAHRAMI A, IN PRESS J ALLOYS CO

[3] Neural networks in materials science [J].