Transactions of FAMENA, Vol. 50 No. 3, 2026.
Original scientific paper
https://doi.org/10.21278/TOF.503085425
A Hybrid FEM-GA-ANN Model for Springback Prediction in Air V-Bending of AISI 1030 Steel
Kemal Yaman
orcid.org/0009-0007-5887-069X
; OSTİM Technical University, Industrial Design Department, 06374, Ankara, Turkey
*
Zafer Tekiner
; Gazi University, Technology Faculty, Department of Manufacturing Engineering, Ankara, Turkey
* Corresponding author.
Abstract
This study examines springback in mild-steel sheet air V-bending through an integrated experimental-numerical-data-driven framework aimed at practical bend design and tool compensation. Bending tests were performed using a modular die set while systematically varying sheet thickness, punch radius, and target bending angle, and repeated measurements were used to ensure reliable springback evaluation. The experimental trends were cross-checked with nonlinear finite element simulations in MSC Marc Mentat®, providing a physics-based reference and confirming that the selected parameters govern the springback response over the investigated range. To enable fast prediction without repeated simulations, a feedforward artificial neural network (ANN) was trained on 106 experimental cases to map the forming parameters to springback. Because conventional ANN training can be sensitive to random initialisation and can become trapped in local minima, a genetic algorithm (GA) was employed to optimise the initial weights and biases prior to gradient-based learning. Compared with a standard ANN, the GA-optimised ANN delivered more stable convergence and improved generalisation, increasing test accuracy (R² from 0.833 to 0.875) and reducing the mean absolute error from 0.195° to 0.075° (61.5% improvement). Overall, the proposed hybrid approach combines experimental reliability, FEM validation, and GA-enhanced learning to provide an efficient and robust springback prediction tool for sheet-metal forming applications.
Keywords
air V-bending; springback; artificial neural network; genetic algorithm; cold forming; finite element analysis
Hrčak ID:
348180
URI
Publication date:
16.6.2026.
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