Transferring local rib responses to new component geometries in sheet forming with a graph-based Transformer

Yingxue Zhao, Haoran Li, Haosu Zhou, Tobias Pfaff, Nan Li

Sheet metal components often consist of a global geometry, such as the corner radii and forming depth of a corner component, and local features added to it. During forming, both affect material flow. A surrogate learning all their combinations may need large training data. However, the same local feature type is often used on many global geometries. This study therefore partly decouples local features from the global geometry. The local-feature effect is first learned on one global geometry, needing fewer simulations than learning all combinations, then transferred to other global geometries via transfer learning, which can need fewer target simulations than training from scratch. A baseline-conditioned residual surrogate, R-BiLoGo, is proposed for ribs on the side walls of hot-stamped corner components. Its inputs are the baseline response and the geometric perturbation introduced by the ribs. Its outputs are the changes in displacement and thinning from the baseline. R-BiLoGo gave lower errors than a hierarchical graph-based model and a Transolver-based model on the source geometry, and on two of three selected target geometries after transfer. Across the 22 target geometries,…