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Adaptive metamaterials by functionally graded 4D printing

delete2017-12-01
delete220
PRE
AI
M
Mahdi Bodaghi
A
A.R. Damanpack
W
Wei‐Hsin Liao *
DOI:10.1016/j.matdes.2017.08.069delete
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摘要

摘要

En 中文
This paper shows how fused decomposition modeling (FDM) as a three-dimensional (3D) printing technology can engineer adaptive metamaterials with performance-driven functionality built directly into materials. The tactic is based on an understanding of thermo-mechanics of shape memory polymers (SMP) and fabrication concept behind FDM as well as experiments to explore how FDM can program self-foldable metamaterials. Self-folding mechanism is investigated in terms of fabrication parameters like printing-speed and liquefier-temperature that affect layer-by-layer programming process and shape-change. It can be called a functionally graded 4D printing so that the structure is fabricated additively and programmed functionally. A finite element (FE) formulation based on the non-linear Green-Lagrange kinematic relations coupled with a robust SMP constitutive model is established to describe material tailoring in fabrication stage and deformation. Governing equations with material-geometric non-linearities are solved by implementing iterative Newton-Raphson method to trace large-deformation non-linear equilibrium path. FDM and FE solution are then applied to digitally design and fabricate straight/curved beams as structural primitives for adaptive metamaterials that show 1D/2D-to-2D/3D shape-shifting by self-folding or/and self-coiling. Finally, it is experimentally shown that the 4D printed metamaterials have great potential in mechanical/biomedical applications like structural/dynamical switches, self-conforming substrates, self-tightening surgical sutures, self-conforming splints and self-coiling/deploying stents. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Material programming
4D printing
SMPs
Self-folding/coiling
Experimental demonstration
FE analysis
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期刊

M
Materials and Design
IF:
7.9
论文数:
1.9W
被引数:
9.8W

机构

C
Chinese University of Hong Kong
学者数:
3.4W
论文数: 3.2W
被引数: 5.6W
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