Print Email Facebook Twitter Inverting the structure–property map of truss metamaterials by deep learning Title Inverting the structure–property map of truss metamaterials by deep learning Author Bastek, Jan Hendrik (ETH Zürich) Kumar, Siddhant (TU Delft Team Sid Kumar) Telgen, Bastian (ETH Zürich) Glaesener, Raphaël N. (ETH Zürich) Kochmann, Dennis M. (ETH Zürich) Date 2022 Abstract Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties. Subject Deep learningInverse designMetamaterialStiffnessTruss To reference this document use: http://resolver.tudelft.nl/uuid:2a2ecbef-8147-4d19-9674-fcead4948094 DOI https://doi.org/10.1073/pnas.2111505119 ISSN 0027-8424 Source Proceedings of the National Academy of Sciences of the United States of America, 119 (1) Part of collection Institutional Repository Document type journal article Rights © 2022 Jan Hendrik Bastek, Siddhant Kumar, Bastian Telgen, Raphaël N. Glaesener, Dennis M. Kochmann Files PDF e2111505119.full.pdf 3.16 MB Close viewer /islandora/object/uuid:2a2ecbef-8147-4d19-9674-fcead4948094/datastream/OBJ/view