Additive manufacturing of cellulose-based materials with continuous, multidirectional stiffness gradients.


Journal

Science advances
ISSN: 2375-2548
Titre abrégé: Sci Adv
Pays: United States
ID NLM: 101653440

Informations de publication

Date de publication:
Feb 2020
Historique:
received: 20 05 2019
accepted: 03 12 2019
entrez: 5 3 2020
pubmed: 5 3 2020
medline: 5 3 2020
Statut: epublish

Résumé

Functionally graded materials (FGMs) enable applications in fields such as biomedicine and architecture, but their fabrication suffers from shortcomings in gradient continuity, interfacial bonding, and directional freedom. In addition, most commercial design software fail to incorporate property gradient data, hindering explorations of the design space of FGMs. Here, we leveraged a combined approach of materials engineering and digital processing to enable extrusion-based multimaterial additive manufacturing of cellulose-based tunable viscoelastic materials with continuous, high-contrast, and multidirectional stiffness gradients. A method to engineer sets of cellulose-based materials with similar compositions, yet distinct mechanical and rheological properties, was established. In parallel, a digital workflow was developed to embed gradient information into design models with integrated fabrication path planning. The payoff of integrating these physical and digital tools is the ability to achieve the same stiffness gradient in multiple ways, opening design possibilities previously limited by the rigid coupling of material and geometry.

Identifiants

pubmed: 32128400
doi: 10.1126/sciadv.aay0929
pii: aay0929
pmc: PMC7034993
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

eaay0929

Informations de copyright

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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Auteurs

P A G S Giachini (PAGS)

Institute for Computational Design and Construction, Faculty of Architecture and Urban Planning, Stuttgart University, Stuttgart, Germany.

S S Gupta (SS)

Institute for Computational Design and Construction, Faculty of Architecture and Urban Planning, Stuttgart University, Stuttgart, Germany.

W Wang (W)

Physical Intelligence Department, Max-Planck Institute for Intelligent Systems, Stuttgart, Germany.

D Wood (D)

Institute for Computational Design and Construction, Faculty of Architecture and Urban Planning, Stuttgart University, Stuttgart, Germany.

M Yunusa (M)

Physical Intelligence Department, Max-Planck Institute for Intelligent Systems, Stuttgart, Germany.

E Baharlou (E)

Institute for Computational Design and Construction, Faculty of Architecture and Urban Planning, Stuttgart University, Stuttgart, Germany.
School of Architecture, University of Virginia, Charlottesville, VA, USA.

M Sitti (M)

Physical Intelligence Department, Max-Planck Institute for Intelligent Systems, Stuttgart, Germany.
School of Medicine and School of Engineering, Koc University, Istanbul, Turkey.

A Menges (A)

Institute for Computational Design and Construction, Faculty of Architecture and Urban Planning, Stuttgart University, Stuttgart, Germany.

Classifications MeSH