4.7 Article

Encoding multiple permanent shapes in 3D printed structures

Journal

MATERIALS & DESIGN
Volume 194, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2020.108888

Keywords

Shape-memory materials; Hierarchical structures; Bioinspiration; Structure-property relationships; 3D printing; Multistability

Funding

  1. Air Force Office of Scientific Research [FA9550-17-1-0074]
  2. Purdue Research Foundation
  3. National Science Foundation
  4. National Science Foundation Graduate Research Fellowship Program [DGE-1333468]
  5. Indiana Space Grant Consortium
  6. School of Mechanical Engineering at Purdue University

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Conventional methods of programmed shape change in polymers are one-way and one-time only. We introduce a new method of combining a bioinspired, hierarchical architecture with the pre-strain generated by extrusion-based 3D printing to encode multiple permanent shapes in thermoplastic polymer shell structures. At high temperature, these structures are multistable and can snap repeatedly between all encoded permanent shapes with ou t reprogramming. At low temperature, the structures are monostable with increased stiffness; the multistability can be switched on and off via temperature. Characterization studies are performed to determine how to control the level of pre-strain and the deflection behavior of the structure to enable finite element model ing for analysis and design. This pre-straining technique may be thought of as a virtual mold: printed structures are initially flat but have multiple permanent shapes encoded. Taking advantage of the geometric freedom of 3D printing, complex geometries, and pre-strain fields may be used to create highly tailored snapping structures. Thermoplastic filaments with added particles may be used to impart additional multifunctionality to the structures, such as magnetic responsiveness, to enable remote actuation. These switchable multistable structures have potential applications in fields including robotics, aerospace, and smart buildings. (c) 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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