Design space and manufacturing of programmable 4D printed continuous flax fibre polylactic acid composite hygromorphs

Charles M Y De Kergariou*, Antoine Le Duigou, Adam W Perriman, Fabrizio Scarpa

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)
67 Downloads (Pure)

Abstract

The work describes the exploration of the design space by fabrication, modelling and testing of bio-based and humidity-triggered 4D printed shape-changing biocomposites. The aim is to broaden the understanding of the control actuation via printing path tailoring and unlock new potential applications for biomaterials and autonomous actuator design. The composites are made with continuous flax yarns and polylactic acid matrix filaments and exhibit moisture-induced actuation. The actuation capability is first demonstrated by printing a calla lily flower-inspired configuration subjected to 98% relative humidity. This structure did not however achieve the anticlastic double curvature and large actuation targeted. To resolve these issues, cross-ply composite architectures with bent filaments deposited in one layer have then been developed. The amplitude for curvature control ranges obtained were 1.9*10−3mm−1 and 7.9*10−3mm−1 depending on the position on the specimen. Other cross-ply hygromorphs solutions are also proposed, with the orientation of their passive layers ([0°]2) tilted by degrees (stacking sequence: [-, 90°]). The largest actuation curvature was obtained when =40°, which increased by 0.0072 mm−1 when compared to = 0°. The hygromorphs presented in this work are modelled using in an in–house filament scale finite element model able to capture the complexity of the printed hygromorphs architectures.
Original languageEnglish
Article number111472
JournalMaterials & Deisgn
Volume225
Early online date14 Dec 2022
DOIs
Publication statusPublished - 1 Jan 2023

Bibliographical note

Funding Information:
The author would like to thank the UK Defence Science and Technology Laboratory for the funding received for this project through the UK-France PhD Scheme. Fabrizio Scarpa also acknowledges the support from ERC-2020-AdG-NEUROMETA (No. 101020715).

Publisher Copyright:
© 2022 The Authors

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