The formation of a hat-profile is significantly influenced by springback and the final cross-sectional geometry, both of which are sensitive to die profile design. This study introduces a scalar-based artificial neural network (ANN) surrogate model combined with genetic-algorithm (GA) optimization to enhance die and process design efficiency. An automated ABAQUS finite-element workflow was established to generate 900 design cases. For each case, seven scalar geometric and angle responses characterizing the post-forming cross section were extracted and used to train a multilayer perceptron. This network maps four die design variables to the final geometry. The surrogate model demonstrated high predictive accuracy, with geometric and angular errors remaining small and coefficients of determination (R²) nearing 1.0. This enabled quick evaluation of new designs without the need for additional finiteelement analyses. By integrating the ANN surrogate within a GA, optimal die geometries were identified that reduce springback while meeting target dimensions, showcasing the proposed framework as an effective AI-driven design tool for sheet-metal forming.

In this paper, the reliability-based parameter study is carried out for the stamping process of a front rail roof member with the ultra high strength steel, considering the scatters of the material properties and the process parameters. With the reliability-based design optimization (RBDO) scheme, the springback tendency is investigated from the perturbation of the process parameters such as the sheet thickness, ultimate tensile strength, yield strength, Coulomb friction coefficient, and applied padding force. The amount of the elastic recovery along the height direction is quantified to describe the springback tendency from the analysis. The analysis shows the springback-amount scattering is not ignorable when the yield stress scatters within the similar range of the ultimate tensile strength. The analysis results fully explain the importance of controlling the scatters as well as the average yield-strength amount in the mass production of the stamped products.

Recently the application of high strength steel sheets, such as DP, TRIP, and TWIP, is rapidly increasing in the automotive industry. Despite this trend, springback is still one of major obstacles that has yet to be overcome. In this study, we conducted U-draw bending experiments and analyzed the process with FEM, focusing on the springback. In the FE analysis, the effects of the hardening model ware studied by comparing the results among an isotropic hardening model, a combined isotropic-kinematic hardening model and the experiment. The effects of the restriking process on shape correction was also =studied by comparing the results of the springback analysis and the experiment.