A multi-level graph-based surrogate model for real-time high-fidelity sheet forming simulations
Year: 2025
Type: article
Abstract: Surrogate models with structured data representations, mainly images and graphs, have been widely investigated in various domains, including automotive manufacturing. Despite advances, existing approaches still face significant challenges in terms of accuracy, efficiency, and generalisability. To address these challenges, a promising direction is to combine the advantages of both images and graphs. This study proposes a graph-based surrogate model, which has a multi-level architecture with enhanced graph convolutional operations using image-inspired spatial edge weights. To evaluate its performance, three additional graph-based surrogate models are developed for comparison, each differing in the formulation of spatial edge weights. All four models are assessed on a real-world hot-stamping B-pillar case study, which involves variations in blank shapes under multiple parameterisations and post-stamped thickness distributions exhibiting complicated local patterns. The proposed architecture significantly outperforms the three comparison models, achieving high accuracy with a relatively low computational burden during training and deployment. Furthermore, it demonstrates strong robustness in hyperparameter calibration and shows the potential for generalisation to other manufacturability-related real-time simulation problems. This study presents an effective methodology for future surrogate model development by integrating the advantages of different structured data representations.
Source: Advanced Engineering Informatics
Institutions: Imperial College London, Google DeepMind (United Kingdom)
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,560
OpenAlex ID: W4410537315