Prediction of the extrusion load and exit temperature using artificial neural networks based on FEM simulation

被引：9
|
作者：
Zhou, J. [1 ,2 ,3 ]
Li, L. [1 ,2 ]
Mo, J. [1 ,2 ]
Zhou, J. [1 ,2 ,3 ]
Duszczyk, J. [3 ]
机构：
[1] State Key Lab Adv Design & Manufacture Vehicle Bo, Changsha, Hunan, Peoples R China
[2] Hunan Univ, coll Mat Sci & Engn, Changsha 410082, Hunan, Peoples R China
[3] Delft Univ Technol, Dept Mat Sci & Engn, Delft, Netherlands
来源：
ADVANCES ON HOT EXTRUSION AND SIMULATION OF LIGHT ALLOYS | 2010年 / 424卷
关键词：
FEM simulation; Artificial neural network; Extrusion; Magnesium; MAGNESIUM ALLOY; HOT EXTRUSION;
D O I：
10.4028/www.scientific.net/KEM.424.241
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
摘要：
In the present study, the extrusion process for the AZ31B magnesium alloy was simulated using a DEFORM-3D software package to establish a database in order to provide input data for artificial neural networks (ANN). The network model was trained by taking extrusion ratio, ram speed, shape complexity and ram displacement as the input variables and the extrusion load and exit temperature as the output parameters. The data from FEM simulations were submitted for ANN as a training file and then ANN built were used to predict the target parameters. The ANN predicted results were found to be in agreement with the FEM simulated and experimental measured ones.
引用
收藏
页码：241 / +
页数：2
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