The properties of conventional materials result from the arrangement of and the interaction between atoms at the nanoscale. Metamaterials have shifted this paradigm by offering property control through structural design at the mesoscale, thus broadening the design space beyond the limits of traditional materials. A family of mechanical metamaterials consisting of soft sheets featuring a patterned array of reconfigurable bistable domes is reported here. The domes in this metamaterial architecture can be reversibly inverted at the local scale to generate programmable multistable shapes and tunable mechanical responses at the global scale. By 3D printing a robotic gripper with energy-storing skin and a structure that can memorize and compute spatially-distributed mechanical signals, it is shown that these metamaterials are an attractive platform for novel mechanologic concepts and open new design opportunities for structures used in robotics, architecture, and biomedical applications.
Materials capable of exhibiting inherent morphing are rare and typically reliant on chemical properties. The resulting diffusion-driven shape adaptability is slow and limited to specific environmental conditions. In contrast, natural composites, such as those found in carnivorous plants, have evolved hierarchical architectures displaying remarkably fast adaptation in response to environmental stimuli. These biological materials have inspired the fabrication of snapping composite shells through the careful design of the internal microstructure of synthetic materials by magnetic alignment of reinforcements. The ability to accurately model such programmable materials using finite element analysis (FEA) is necessary to facilitate the design optimization of the resulting structures. Using similar material parameters as explored in previous experimental studies, we employ nonlinear FEA to investigate the effects of introducing curvilinear spatially distributed micro-reinforcements on the deformation of a shell with an unusual bioinspired geometry. The FEA model is subject to experimental validation with magnetically aligned specimens. Comparison to a traditional [90/0] composite layup demonstrates the advantages of magnetically aligned reinforcements to achieve complex, snapping morphing structures with tailored characteristics.
Physical systems exhibiting neuromechanical functions promise to enable structures with directly encoded autonomy and intelligence. A neuromorphic metamaterials class embodying bioinspired mechanosensing, memory, and learning functionalities obtained by leveraging mechanical instabilities integrated with memristive materials is reported. The prototype system comprises a multistable metamaterial whose bistable dome‐shaped units collectively filter, amplify, and transduce external mechanical inputs over large areas into simple electrical signals using embedded piezoresistive sensors. Dome deformations in nonvolatile memristors triggered by the transduced signals, providing a means to store loading events in measurable material states are recorded. Sequentially applied mechanical inputs result in accumulated memristance changes that allow us to physically encode a Hopfield network into the neuromorphic metamaterials. This physical network learns the history of spatially distributed input patterns. Crucially, the neuromorphic metamaterials can retrieve the learned patterns from the memristors’ final accumulated state. Therefore, the system exhibits the ability to learn without supervised training and retain spatially distributed inputs with minimal external overhead. The system's embodied mechanosensing, memory, and learning capabilities establish an avenue for synthetic neuromorphic metamaterials that learn via tactile interactions. This capability suggests new types of large‐area smart surfaces for robotics, autonomous systems, wearables, and morphing structures subjected to spatiotemporal mechanical loading.
In article number 2001955, André R. Studart, Andres F. Arrieta, and co‐workers demonstrate a family of mechanical metamaterials exploiting sheets patterned with locally bistable domes. The metamaterial's strong hierarchical response enables tunable mechanical properties and programmed geometry adaptation. The strain energy stored by local dome inversion simplifies soft robotics grippers' actuation and enables memory and computation of spatially‐distributed mechanical signals in thin skins.
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