Integration of feedforward neural network and finite element in the draw-bend springback prediction

被引：19

作者：

Jamli, M. R. ^{[1
,2
]}

Ariffin, A. K. ^{[1
]}

Wahab, D. A. ^{[1
]}

机构：

[1] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Mech & Mat Engn, Ukm Bangi 43600, Selangor, Malaysia

[2] Univ Teknikal Malaysia Melaka, Fac Mfg Engn, Dept Mfg Proc, Durian Tunggal 76100, Melaka, Malaysia

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2014年 / 41卷 / 08期

关键词：

Finite element; Neural network; Nonlinear elastic recovery; Springback prediction; CONSTITUTIVE MODEL; STEEL SHEETS; BEHAVIOR; ALLOY; IDENTIFICATION; DEFORMATION; MODULUS;

D O I：

10.1016/j.eswa.2013.12.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To achieve accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an alternative modelling method able to facilitate nonlinear recovery was developed for springback prediction. The nonlinear elastic recovery was processed using back-propagation networks in an artificial neural network (ANN). This approach is able to perform pattern recognition and create direct mapping of the elastically-driven change after plastic deformation. The FE program for the sheet metal springback experiment was carried out with the integration of ANN. The results obtained at the end of the FE analyses were found to have improved in comparison to the measured data. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：3662 / 3670

页数：9

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