To support die-motion design for double-sided symmetric forming of tubular stabilizer bars, this study developed an integrated finite-element (FE)–artificial neural network (ANN) framework for predicting springback and cross-sectional deformation. An R45 Abaqus model was calibrated against tensile responses from three circumferential positions. Free-end simulations established a neutral material inflow of 7.32 mm per 10° of bending. In the fixed-end database analyses, the bending die and support block were translated together by the prescribed axial displacement. Twelve commanded angles and five die-feed offsets generated a 60-case database. After min-max normalization, the 48 development cases underwent five-fold cross-validation, and the 12 holdout cases were reserved for one-time final evaluation. Across the four responses, mean cross-validation R² values ranged from 0.849 to 0.967, and holdout R² values from 0.913 to 0.976. The 2-20-20-4 ANN was then retrained using all 60 cases. R45 fixed-end FE results under the neutral die-feed condition yielded a springback RMSE of 0.275° and a MAPE of 6.29%. Over 10–120°, the maximum relative deviations were 3.52% (minimum outer diameter), 3.65% (minimum wall thickness), and 3.10% (maximum wall thickness). The final surrogate model supports rapid response estimation and angle compensation for new input combinations within the FE design space.
