Soft Kirigami Composites that Deploy into Pre-programmed 3D Shapes

Who is working on this project: Leixin Ma

Fully soft bistable mechanisms have shown extensive applications ranging from soft robotics, wearable devices, and medical tools, to energy harvesting. However, the lack of systematic theoretical analysis, design strategies and fabrication methods that are easy and potentially scalable limits their further adoption into mainstream applications.

We introduce a new class of thin flexible structures that can morph a flat shape into a prescribed 3D shape without an external stimulus such as mechanical loads or heat. 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 mode shapes. 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.

(2) ML-based inverse design

Can soft structures of arbitrary shapes be designed and manufactured entirely in a 2D plane?

Soft deployable structures have infinite degrees of freedom, which helps expand the functionalities of structures. However, the high dimensionality causes designing soft deployable structures challenging, which used to be a process of trial and error with complex local actuation and fabrication.

We first report a novel design procedure that combines planar manufacturing technologies with an active learning algorithm. To relax the need for local actuation, we develop a much-simplified planar fabrication approach that combines the strain mismatch in the composite structure and kirigami designs. To expedite the design process and explore the capability of such a much-simplified fabrication approach, we develop and apply an active learning approach to optimize the design parameters to achieve target-free buckling shapes. By exploring the nonlinear interplay between kirigami patterns and strain-mismatch, we can create a wide range of 3D shapes