A multi-level graph-based surrogate model for real-time high-fidelity sheet forming simulations
File(s)
Published version
OA Location
Author(s)
Zhou, Haosu
Zhao, Yingxue
Li, Haoran
Pfaff, Tobias
Li, Nan
Type
Journal 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.
Date Issued
2025-07-01
Date Acceptance
2025-05-06
Citation
Advanced Engineering Informatics: the science of supporting knowledge-intensive activities, 2025, 66
ISSN
0954-1810
Publisher
Elsevier
Journal / Book Title
Advanced Engineering Informatics: the science of supporting knowledge-intensive activities
Volume
66
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.aei.2025.103458
Publication Status
Published
Article Number
103458
Date Publish Online
2025-05-20