Inverse design and additive manufacturing of shape-morphing structures based on functionally graded composites

Kansara, Hirak; Liu, Mingchao; He, Yinfeng; Tan, Wei

Abstract

Shape-morphing structures possess the ability to change their shapes from one state to another, and therefore, offer great potential for a broad range of applications. A typical paradigm of morphing is transforming from an initial two-dimensional (2D) flat configuration into a three-dimensional (3D) target structure. One popular fabrication method for these structures involves programming cuts in specific locations of a thin sheet material (i.e. kirigami), forming a desired 3D shape upon application of external mechanical load. By adopting the non-linear beam equation, an inverse design strategy has been proposed to determine the 2D cutting patterns required to achieve an axisymmetric 3D target shape. Specifically, tailoring the localised variation of bending stiffness is the key requirement. In this paper, a novel inverse design strategy is proposed by modifying the bending stiffness via introducing distributed modulus in functionally graded composites (FGCs). To fabricate the FGC-based shape-morphing structures, we use a multi-material 3D printer to print graded composites with voxel-like building blocks. The longitudinal modulus of each cross-sectional slice can be controlled through the rule of mixtures according to the micro-mechanics model, hence matching the required modulus distribution along the elastic strip. Following the proposed framework, a diverse range of structures is obtained with different Gaussian curvatures in both numerical simulations and experiments. A very good agreement is achieved between the measured shapes of morphed structures and the targets. In addition, the compressive rigidity and specific energy absorption during compression of FGC-based hemi-ellipsoidal morphing structures with various aspect ratios were also examined numerically and validated against experiments. By conducting systematical numerical simulations, we also demonstrate the multifunctionality of the modulus-graded shape-morphing composites. For example, they are capable of blending the distinct advantages of two different materials, i.e. one with high thermal (but low electrical) conductivity, and the other is the other way around, to achieve combined effective properties in a single structure made by FGCs. This new inverse design framework provides an opportunity to create shape-morphing structures by utilising modulus-graded composite materials, which can be employed in a variety of applications involving multi-physical environments. Furthermore, this framework underscores the versatility of the approach, enabling precise control over material properties at a local level.

Citation

Kansara, H., Liu, M., He, Y., & Tan, W. (2023). Inverse design and additive manufacturing of shape-morphing structures based on functionally graded composites. Journal of the Mechanics and Physics of Solids, 180, Article 105382. https://doi.org/10.1016/j.jmps.2023.105382

• Journal Article Type: Article
• Acceptance Date: Jul 7, 2023
• Online Publication Date: Jul 20, 2023
• Publication Date: 2023-11
• Deposit Date: May 5, 2024
• Publicly Available Date: May 8, 2024
• Journal: Journal of the Mechanics and Physics of Solids
• Print ISSN: 0022-5096
• Electronic ISSN: 0022-5096
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 180
• Article Number: 105382
• DOI: https://doi.org/10.1016/j.jmps.2023.105382
• Keywords: Functionally graded composites; Shape-morphing structures; Inverse design; Additive manufacturing; Multifunctionality
• Public URL: https://nottingham-repository.worktribe.com/output/34352068
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0022509623001862?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Inverse design and additive manufacturing of shape-morphing structures based on functionally graded composites; Journal Title: Journal of the Mechanics and Physics of Solids; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jmps.2023.105382; Content Type: article; Copyright: © 2023 The Authors. Published by Elsevier Ltd.