Adaptive metamaterials by functionally graded 4D printing

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ID: 24935
2017
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Abstract
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. Keywords: Material programming, 4D printing, SMPs, Self-folding/coiling, Experimental demonstration, FE analysis
Reference Key
bodaghi2017adaptivematerials Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bodaghi, M.;Damanpack, A.R.;Liao, W.H.;
Journal materials & design
Year 2017
DOI
DOI not found
URL
http://www.sciencedirect.com/science/article/pii/S0264127517308237
Keywords Keywords not found

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