| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Design of Three-dimensional Complex Truss Metamaterials with Graph Neural Networks |
| Author(s) |
Marco Maurizi, Desheng Yao, Xiaoyu (Rayne) Zheng |
| On-Site Speaker (Planned) |
Marco Maurizi |
| Abstract Scope |
The rapid development of additive manufacturing technologies has enabled the fabrication of truss metamaterials, i.e., a novel class of lightweight-yet-strong materials with engineered complex hierarchical structures. Manipulating the architecture over chemical composition dramatically expands the achievable materials design space, allowing to largely control the mechanical response of metamaterials. Despite the great advances made in this area, designing three-dimensional (3D) truss metamaterials under complex or extreme conditions with programmable response is still a challenge. Here we propose a paradigm to design 3D truss metamaterials with complex programmable mechanical responses both under quasi-static and dynamic loading based on graph neural networks (GNNs). By leveraging the ability of our GNN-based model to accurately predict the mechanical response across multiple orders of magnitude, we inverse design truss metamaterials for compressive loading up to 50 % of strain and dynamic transmissibility with desired band gaps, opening the way for full materials design freedom. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |