RUGNN, a recurrent U-Net graph neural network with a node-to-surface contact feature, predicts sheet metal deformation fields across stamping timesteps with lower accumulated error than three GNN baselines on two FE-based forming datasets.
A new design guideline development strategy for aluminium alloy corners formed through cold and hot stamping processes,
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Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming
RUGNN, a recurrent U-Net graph neural network with a node-to-surface contact feature, predicts sheet metal deformation fields across stamping timesteps with lower accumulated error than three GNN baselines on two FE-based forming datasets.