Rapid prediction of material deformation in hot stamping of battery box geometries using graph neural network

The development of lightweight robust structures for battery box is critical for enhancing the performance and energy efficiency of electric vehicles. Hot stamping technology is widely used to form these geometries from high strength-to-weight materials. Recent efforts have leveraged surrogate models to predict material deformation behaviours, offering critical insights into the design of component geometries. However, most surrogate models rely on image-based data representations, which faces challenges in feature representation and permutation invariance. To address these challenges, this study introduces a Recurrent U Net-based Graph Neural Network (RUGNN) surrogate model. The RUGNN model is designed to make spatial-temporal prediction of material deformation under varying contact conditions imposed by different forming tool geometries. This model enables rapid and accurate predictions of spatial-temporal deformation fields under hot stamping conditions. It allows designers to quickly evaluate the effects of forming tools geometry on blank material deformation behaviour and optimise designs during early-stage exploration. Training is conducted on a diverse dataset of deep-drawn corner geometries, which serve as a typical demonstrator in battery box design. The network predictions closely match the ground truth from FE simulations. The RUGNN framework supports early-stage tool design explorations and enables efficient evaluation of complex geometries.