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Abstract A new class of thin flexible structures is introduced that morph from flat into prescribed 3D shapes through strain mismatch between layers of a composite plate. To achieve control over the target shape, two different concepts are coupled. First, motivated by biological growth, strain mismatch is applied between the flat composite layers to transform it into a 3D shape. Depending on the amount of the applied strain mismatch, the transformation involves buckling into one of the available finite number of deformation modes. Second, inspired by kirigami, portions of the material are removed from one of the layers according to a specific pattern. This dramatically increases the number of possible 3D shapes and allows us to attain specific topologies. An experimental apparatus that allows precise control of the strain mismatch is devised. An inverse problem is posed, where starting from a given target shape, the physical parameters that make these shapes possible are determined. To show how the concept works, it focuses on circular composite plates and designs a kirigami pattern that yields a hemispherical structure. The analysis combines a theoretical approach with numerical simulations and physical experiments to understand and predict the shape transition from 2D to 3D. The tools developed here can be extended to attain arbitrary 3D shapes. The initially flat shape suggests that conventional additive manufacturing techniques can be used to functionalize the soft kirigami composite to fabricate, for example, deployable 3D structures, smart skins, and soft electromagnetic metasurfaces.
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Inverse Design of Inflatable Soft Membranes Through Machine Learning
Abstract Across fields of science, researchers have increasingly focused on designing soft devices that can shape‐morph to achieve functionality. However, identifying a rest shape that leads to a target 3D shape upon actuation is a non‐trivial task that involves inverse design capabilities. In this study, a simple and efficient platform is presented to design pre‐programmed 3D shapes starting from 2D planar composite membranes. By training neural networks with a small set of finite element simulations, the authors are able to obtain both the optimal design for a pixelated 2D elastomeric membrane and the inflation pressure required for it to morph into a target shape. The proposed method has potential to be employed at multiple scales and for different applications. As an example, it is shown how these inversely designed membranes can be used for mechanotherapy applications, by stimulating certain areas while avoiding prescribed locations.
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- PAR ID:
- 10367764
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Functional Materials
- Volume:
- 32
- Issue:
- 16
- ISSN:
- 1616-301X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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