Abstract Accurate prediction of the Forming Limit Curve (FLC) is essential for the design of sheet metal stamping processes; however, its experimental determination is costly and limited by data availability. This work investigates the use of Machine Learning techniques to predict the FLC of Dual Phase (DP) steels based on mechanical properties obtained from uniaxial tensile tests. To overcome the scarcity of experimental data, a synthetic database was developed based on statistical consistency and physical constraints, using Kernel Density Estimation, PCA projections, and controlled probabilistic interpolation, followed by the application of physicometallurgical plausibility criteria. The models use physics-based descriptors as input variables, which reflect known metallurgical mechanisms associated with plastic instability, without explicitly incorporating differential equations into the training process. The results show that all models were able to reproduce the characteristic geometry of the FLC, with errors on the order of 10⁻³–10⁻². Among the investigated techniques, Random Forest exhibited the best performance (MAE = 0.0052; MSE = 0.00011; R² = 0.943), followed by XGBoost, while the Neural Network showed greater variability and a tendency toward overfitting. The results demonstrate that the combination of physics-based descriptors, statistically validated synthetic expansion, and ensemble machine learning methods constitutes a robust and efficient strategy for modeling FLCs of DP steels.
Rosiak et al. (Sun,) studied this question.