Soft Kirigami Composites that Deploy into Pre-programmed 3D Shapes
Who is working on this project: Leixin Ma
Soft Kirigami Composites:
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.
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
GAN-based Inverse Design
The design and development of morphing structures that transition from compact, transportable forms to stable, deployable configurations is crucial for advances in soft robotics, healthcare applications, and biomimetic systems. These structures often require customized functionalities and must self-deploy into precise target shapes. Therefore, the deformed shapes of such structures are usually prescribed and the parameters for their design are unknown. To obtain the fabrication parameters, the inverse problem needs to be solved, which quickly becomes quite challenging using conventional methods due to the high-dimensional nature of the inverse problem as well as the material and geometric nonlinearities. To overcome these challenges, we combine the best of the two worlds – physics and data – and present a data-driven approach for the inverse design of two-layered soft composites that utilize the principles of kirigami and strain mismatch to self-deploy into different three-dimensional shapes. At the center of our methodology is the generative adversarial network, designed to generate the necessary fabrication parameters. By using a pre-trained simulator network, we condition the generative model to generate feasible and accurate fabrication parameters that are used to make composites that deploy into the target shapes. Our findings demonstrate that the generative model is able to effectively predict kirigami patterns and pre-stretch values required to realize complex three-dimensional shapes from simple and diverse planar designs. By performing simulations and precise desktop experiments, we compare the target with deployed shapes and demonstrate the predictive capacity of the method.
Publication: Ma, L., Mungekar, M., Roychowdhury, V., & Jawed, M. K., “Rapid design of fully soft deployable structures via kirigami cuts and active learning” Advanced Materials Technologies, 2301305 (2024)
Design soft structures of arbitrary target shapes using planer fabrication strategies.
Publication: Ma, L., Mungekar, M., Roychowdhury, V., & Jawed, M. K., “Rapid design of fully soft deployable structures via kirigami cuts and active learning” Advanced Materials Technologies, 2301305 (2024)
Funding Source: We are grateful for the financial support from the National Science Foundation (Award numbers: CMMI-2053971).