Inverting the structure-property map of truss metamaterials by deep learning.
Proc Natl Acad Sci U S A
; 119(1)2022 01 04.
Article
en En
| MEDLINE
| ID: mdl-34983845
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Materiales de Construcción
/
Aprendizaje Profundo
/
Modelos Teóricos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Año:
2022
Tipo del documento:
Article