Inverse technique identification of material parameters using finite element and neural network computation

被引：30
|
作者：
Chamekh, A. [2 ]
Salah, H. Bel Hadj [2 ]
Hambli, R. [1 ]
机构：
[1] Polytech Orleans LMSP Prisme, F-45072 Orleans 2, France
[2] ENIM LGM, Monastir 5019, Tunisia
来源：
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2009年 / 44卷 / 1-2期
关键词：
Finite element; Neural networks; Deep drawing; Anisotropy; Inverse technique; PREDICTION;
D O I：
10.1007/s00170-008-1809-6
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Experimental identification of anisotropic behavior law is currently obtained by performing several complicated tests and a long duration of experiments. This paper describes a new technique allowing for the identification of HILL anisotropic parameters by inverse technique method based on deep drawing of a cylindrical cup. The identification approach is based on the artificial neural network (ANN) computation trained from finite element simulation. The results obtained by ANN models and by the finite element method shows a good agreement.
引用
收藏
页码：173 / 179
页数：7
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