Integration of feedforward neural network and finite element in the draw-bend springback prediction
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 predict...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2013
|
| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/17851/1/Jamli%202014.pdf http://eprints.utem.edu.my/id/eprint/17851/ http://www.sciencedirect.com/science/article/pii/S0957417413009706 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 elasticallydriven
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. |
|---|