Neural network prediction of conversion rate of TbFe2 alloy prepared by reduction-diffusion process

被引：0
|
作者：
Guo, Guangsi [1 ]
Wang, Guangtai [1 ]
Cheng, Yongjun [2 ]
Hu, Xiaomei [1 ]
机构：
[1] Shenyang Ligong University, Shenyang,110159, China
[2] Shenyang Aerospace Mitsubishi Motors Engine Manufacturing Co. Ltd, Shenyang,110179, China
来源：
Xiyou Jinshu Cailiao Yu Gongcheng/Rare Metal Materials and Engineering | 2015年 / 44卷 / 05期
关键词：
Particle size - Forecasting - Neural networks - Iron alloys;
D O I：
暂无
中图分类号：
TF6 [铁合金冶炼];
学科分类号：
080602 ;
摘要：
A BP neural network was established based on the following main experiment parameters of producing TbFe2 alloy by reduction-diffusion process: reaction temperature, holding time, quantity of Ca and particle size of Fe. A simulation was conducted, and the rate of conversion of TbFe2 alloy was predicted. The neural network was simulated and tested by 44 groups of experimental data. It can be concluded that the neural network has good performance to predict the rate of conversion of TbFe2 alloy. The design and the application of this neural network can help to shorten the periodic time of experiments, lower the experimental cost, and optimize the preparation processes. Copyright © 2015, Northwest Institute for Nonferrous Metal Research. Published by Elsevier BV. All rights reserved.
引用
收藏
页码：1104 / 1107
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