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ORIGINAL PAPER
Springback prediction of sheet metal hydroforming using finite element analysis and artificial neural networks
1
Génie Mécanique, ENSEM, Hassan II University, B.P 8118 Oasis, Casablanca, Morocco.
LMPGI, Higher School of Technology of Casablanca, ESTC, Hassan II University, B.P 8112 Oasis, Casablanca, Morocco., Morocco
2
Génie Mécanique, ENSEM, Hassan II University, B.P 8118 Oasis, Casablanca, Morocco.
LMPGI, Higher School of Technology of Casablanca, ESTC, Hassan II University, B.P 8112 Oasis, Casablanca, Morocco
3
Génie Mécanique, LMPGI, Ecole Supérieure de Technologie de Casablanca, ESTC, Université Hassan II, BP 8112 Oasis, Casablanca, Maroc,
Submission date: 2025-02-04
Final revision date: 2025-04-04
Acceptance date: 2025-05-22
Online publication date: 2025-09-02
Publication date: 2025-09-02
Corresponding author
YASSINE FARTOUH
Génie Mécanique, ENSEM, Hassan II University, B.P 8118 Oasis, Casablanca, Morocco. LMPGI, Higher School of Technology of Casablanca, ESTC, Hassan II University, B.P 8112 Oasis, Casablanca, Morocco., Morocco
Génie Mécanique, ENSEM, Hassan II University, B.P 8118 Oasis, Casablanca, Morocco. LMPGI, Higher School of Technology of Casablanca, ESTC, Hassan II University, B.P 8112 Oasis, Casablanca, Morocco., Morocco
International Journal of Applied Mechanics and Engineering 2025;30(3):42-56
KEYWORDS
TOPICS
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.
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