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.
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