@article{Luo:2026:10.1016/j.matdes.2026.117298,
author = {Luo, J and Mohamed, M and Hassan, O and Zhou, H and Zhao, Y and Li, H and Li, X and Shao, Z and Long, Y and Li, J and Li, N},
doi = {10.1016/j.matdes.2026.117298},
journal = {Materials & Design},
title = {StampFormer: a physics-guided material-geometry-coupled multimodal model for rapid prediction of physical fields in sheet metal stamping},
url = {http://dx.doi.org/10.1016/j.matdes.2026.117298},
volume = {272},
year = {2026}
}

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TY  - JOUR
AB  - Traditional sheet metal forming relies on computationally expensive Finite Element Analysis (FEA), significantly prolonging design cycles. While surrogate models accelerate iteration, existing approaches either provide limited scalar outputs or ignore crucial material properties by focusing solely on geometry. To address this gap, we develop StampFormer, a physics-guided deep learning framework that simultaneously processes component geometry and material stress-strain responses to predict FEA outcomes. Its architecture employs a Material-Augmented Geometric Network (MAGN) for initial multimodal fusion, followed by a Hierarchical Material Embedding Injection Unit (HMEIU) that progressively integrates material features into an adapted Swin-UNet backbone. Evaluated on steel and aluminium crossmember stamping datasets, StampFormer predicts critical physical fields—including thinning, major/minor strain, plastic strain, and displacement—in under a second. Compared with the reference FEA, our model achieves a mean representative-maximum relative error below  for each of the four 2D fields in both material datasets and a masked displacement mean squared error below  mm2. Within the investigated component family and fixed process conditions, this multimodal framework provides sub-second predictions for rapid early-stage material–geometry screening and design exploration.
AU  - Luo,J
AU  - Mohamed,M
AU  - Hassan,O
AU  - Zhou,H
AU  - Zhao,Y
AU  - Li,H
AU  - Li,X
AU  - Shao,Z
AU  - Long,Y
AU  - Li,J
AU  - Li,N
DO  - 10.1016/j.matdes.2026.117298
PY  - 2026///
SN  - 0264-1275
TI  - StampFormer: a physics-guided material-geometry-coupled multimodal model for rapid prediction of physical fields in sheet metal stamping
T2  - Materials & Design
UR  - http://dx.doi.org/10.1016/j.matdes.2026.117298
UR  - https://doi.org/10.1016/j.matdes.2026.117298
VL  - 272
ER  - 

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