Digital Library of Zielona Góra contains 63 052 digital objects
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Title: Springback prediction of sheet metal hydroforming using finite element analysis and artificial neural networks
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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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
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DOI:
Pages:
Source:
IJAME, volume 30, number 3 (2025)
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License:
License CC BY-NC-ND 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Digital Library of Zielona Góra > Repository > Faculties > Faculty of Mechanical Engineering
- Digital Library of Zielona Góra > Repository > Types of work > Articles
- Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mechanics and Engineering (IJAME)
- Digital Library of Zielona Góra > 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:
Sep 5, 2025
In our library since:
Sep 5, 2025
Number of object content hits:
50
All available object's versions:
https://zbc.uz.zgora.pl/publication/102669
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| Edition name | Date |
|---|---|
| Springback prediction of sheet metal hydroforming using finite element analysis and artificial neural networks | Sep 5, 2025 |
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