A HYBRID FEM-GA-ANN MODEL FOR SPRINGBACK PREDICTION IN AIR V-BENDING OF AISI 1030 STEEL

Yaman K., TEKİNER Z.

TRANSACTIONS OF FAMENA, cilt.50, sa.3, ss.31-51, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 3
  • Basım Tarihi: 2026
  • Doi Numarası: 10.21278/tof.503085425
  • Dergi Adı: TRANSACTIONS OF FAMENA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
  • Sayfa Sayıları: ss.31-51
  • Gazi Üniversitesi Adresli: Evet

Özet

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 crosschecked with nonlinear finite element simulations in MSC Marc Mentat (R), providing a physicsbased 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 degrees to 0.075 degrees (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.