Digital Library of Zielona Góra contains 9 272 digital objects
Object
Title: Springback prediction of sheet metal hydroforming using finite element analysis and artificial neural networks
Współtwórca:
Tytuł publikacji grupowej:
Abstract:
The objective of this paper is to develop a method for the rapid estimating springback in the hydroforming process of circular sheets. First, the springback behavior has been studied with using finite element simulations for various configurations such as sheet thickness, sheet diameter, and deformation pressure. The results obtained shows an excellent correlation with the experimental data. ; Next, the springback of circular sheets in the setting of hydroforming has been predicted using the artificial neural networks (ANN) approach. Statistical measures, specifically the mean square error (MSE) and the coefficient (R2) are implemented for evaluating this approach. The results reveal that artificial neural networks provide an accurate, high-performance model for predicting the springback of circular sheets.
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Format:
Identyfikator zasobu:
DOI:
Strony:
Źródło:
IJAME, volume 30, number 3 (2025)
Jezyk:
Licencja:
Licencja CC BY-NC-ND 4.0:
Prawa do dysponowania publikacją:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Mechanical Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mechanics and Engineering (IJAME)
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mechanics and Engineering (IJAME) > International Journal of Applied Mechanics and Engineering (IJAME) (2025)
Last modified:
5 wrz 2025
In our library since:
5 wrz 2025
Number of object content hits:
50
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/102669
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Show description in OAI-PMH format:
| Edition name | Date |
|---|---|
| Springback prediction of sheet metal hydroforming using finite element analysis and artificial neural networks | 5 wrz 2025 |
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