Developing bistable metamaterials has recently offered a new design paradigm for deployable structures and reusable dampers. While most bistable mechanisms possess inclined/curved struts, a new 3D multistable shellular metamaterial is developed by introducing delicate perforations on the surface of Schwarz's Primitive shellular, integrating the unique properties of shellular materials such as high surface area, stiffness, and energy absorption with the multistability concept. Denoting the fundamental snapping part by motif, certain shellular motifs with elliptical perforations exhibit mechanical bistability. To bring the concept of multistability to a single motif, multistable shellular motifs are developed by introducing multilayer staggered perforations that form hinges and facilitate local instability. Adopting an n‐layer staggered perforation (n hinges) design leads to a maximum 2n−1 stable states within one shellular motif during loading and unloading. Three‐directional multistable shellulars are attained by extending the perforation design in three orthogonal directions. Harnessing snap‐through and snap‐back behaviors and self‐contact, the introduced multistable perforated shellulars exhibit strong rigidity both in loading and unloading, and enhanced energy dissipation. The introduced design strategy opens up new horizons for creating multidirectional multistable metamaterials with load bearing capabilities for applications in soft robotics, shape‐morphing architectures, and reusable and deployable energy absorbers/dampers.
Using multistable mechanical metamaterials to develop deployable structures, electrical devices, and mechanical memories raises two unanswered questions. First, can mechanical instability be programmed to design sensors and memory devices? Second, how can mechanical properties be tuned at the post‐fabrication stage via external stimuli? Answering these questions requires a thorough understanding of the snapping sequences and variations of the elastic energy in multistable metamaterials. The mechanics of deformation sequences and continuous force/energy–displacement curves are comprehensively unveiled here. A 1D array, that is chain, of bistable cells is studied to explore instability‐induced energy release and snapping sequences under one external mechanical stimulus. This method offers an insight into the programmability of multistable chains, which is exploited to fabricate a mechanical sensor/memory with sampling (analog to digital‐A/D) and data reconstruction (digital to analog‐D/A) functionalities operating based on the correlation between the deformation sequence and the mechanical input. The findings offer a new paradigm for developing programmable high‐capacity read–write mechanical memories regardless of thei size scale. Furthermore, exotic mechanical properties can be tuned by harnessing the attained programmability of multistable chains. In this respect, a transversely multistable mechanical metamaterial with tensegrity‐like bistable cells is designed to showcase the tunability of chirality.
Programmable Materials In article number 2102423, Abdolhamid Akbarzadeh and co‐workers report the development of the first multistable perforated P shellular by introducing multilayer staggered perforations that form hinges and facilitate local instability in three orthogonal directions. This work provides a systematic approach for the realization of multistable perforated P shellulars, in which all snap‐through, snap‐back, self‐contact, and number of stable configurations can be programmed.
This research relates the morphology of the structural network of a dragonfly wing to the static equilibrium of forces using a geometry-based equilibrium method known as graphic statics. It then develops machine learning algorithms to generate similar structural networks for designing airplane wings using the in-plane equilibrium information of the dragonfly wing. This approach can generate a structural network in equilibrium with no prior information related to the topology or geometry of the network just by receiving the boundary geometry of the wing. This research shows that the internal network of the dragonfly wing can be assumed to be compression or tension-only system on a 2D plane. Consequently, another diagram is constructed to represent the geometric equilibrium of the forces in the initial network. This diagram is called the force diagram in the context of graphic statics. A new wing geometry is reconstructed from the force diagram, with its members sized according to the force magnitude. Both form of the network and its force diagram are used to train machine learning models for the generation of the structural network of the wing. The developed methodology is also capitalized to generate microstructural patterns inspired by other species with networks of convex polygons. We numerically and experimentally examine one application of this method in designing the cellular core, 3D printed by fused deposition modeling, of the airfoil wing, which suggests up to 25% improvement in the out-of-plane stiffness. Our findings suggest this ML-assisted approach enables leveraging million years of evolution in nature to develop the next generation of high-performance, lightweight structures.
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