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<p>Structures with inherent shape change capabilities enable adaptive, efficient designs without the weight and complexity of external actuators and sensors. Morphing structures are found in nature: plants are able to achieve fast motion without muscular or nervous systems. For example, the Venus flytrap snaps to a closed state with spatially distributed curvatures in less than one second. In contrast, synthetic shape change has been limited by a trade-off between complexity and speed. Shape memory polymers (SMPs) can remember complex shapes, but morphing is slow and one-way. Multistability due to mechanical buckling is fast and reversible, but it has been limited to simple shapes. Furthermore, many examples of biological shape change follow logical patterns with mechanisms that selectively respond to environmental stimuli. This suggests that synthetic morphing structures may also lend themselves to alternative forms of sensing, memory, and logic.</p> <p><br></p> <p>In this research, we introduce a new method of using SMPs in combination with the hierarchical architectures of pre-strained multistable laminates to create switchable multistable structures (SMS). An SMS can remember multiple permanent shapes and reversibly snap between them. We use extrusion-based 3D printing to encode contrasting shape memory-based pre-strain fields in a bilayer. Above the SMP’s glass transition temperature, the SMS becomes compliant and remembers multiple encoded permanent shapes with fast snap-through between them. Below the transition temperature, the SMS regains its stiffness and is fixed in a single state. The geometric freedom of 3D printing enables the design and manufacture of bioinspired structures with complex pre-strain fields and deflections. The developed printing method is applied in multiple subsequent studies, including mechanical pixels, self-folding spring origami structures, and multistable structures printed with thermoset composite inks. </p> <p><br></p> <p>The highly nonlinear behavior of bistable, pre-strained structures makes their design difficult and nonintuitive. Generally, these structures are designed using a slow, iterative process with finite element analysis (FEA). We aim to solve the inverse optimization problem: start with target stable states and solve for the necessary pre-strain distributions. To this end, we develop and implement the switching tunneling method (STM) to design pre-strained,</p> <p>multistable structures. Instead of FEA, we leverage analytical solutions for gradient-based optimization. Tunneling allows for the efficient search of a design space which may contain multiple local and global minima. Switching enables us to take advantage of two different function transformations, depending on if the search is far from or close to a minimum. The STM is validated through FEA and experiments for both conventional and variable</p> <p>pre-strain bistable structures.</p> <p><br></p> <p>Structures designed to react to external conditions or events offer the opportunity to directly integrate sensing, memory, and computation into a structure. This concept is explored using metasheets composed of locally bistable unit cells, which display spatiotemporal mechanical sensing (mechanosensing) and memory. A unit cell consists of a bistable dome with a piezoresistive strip at the base; the resistance indicates the state of the dome. The mechanics of bistability offer inherent filtering and nonlinear signal amplification capabilities, tunable via geometric parameters. Metasheet arrays of these unit cells display distributed sensing capabilities, as well as hierarchical multistability.</p> <p><br></p> <p>We explore the use of time-dependent material properties combined with the mechanics of multistability to encode many unique values within a single mechanosensor unit cell, beyond binary memory. When the piezoresistive material is viscoelastic, cyclic loading causes cumulative changes in both the ground and inverted state resistances. Effectively, the metamaterial is able to count how many times an external force has been applied; this count is stored in the metamaterial’s intrinsic, measurable properties.</p> <p><br></p> <p>This work demonstrates the importance of incorporating memory concepts into structural design, which enables multistability with complex stable shapes, as well as spatiotemporal sensing and memory capabilities. Engineered systems require increasingly adaptive and responsive structures to improve efficiency. The incorporation of inherent memory and sensing enables the complex behaviors needed to interact with unstructured environments</p> <p>and biological features, a pressing issue for aerospace, soft robotics and biomedical devices. The methodology developed here to manufacture, design, and analyze multistable structures advances the state of the art and makes their implementation more practical.</p>

<p>Soft robotics is concerned with the modeling and designing of devices fabricated from materials with low Young’s moduli—much less than that of metal— that mimic the input/output operation and physical task utility of robotics.  The inherent compliance of soft robots lends these devices an adaptability and a capacity for human-machine interaction beyond that of conventional robotics. Multistable soft robotic grippers are a subset of the technology at the intersection of soft robotics and multistable structures. Multistable structures are continuum systems that exhibit more than one statically stable state, each associated with a strain energy minimum. The existence of these energetic minima allows the structures to adopt different stable configurations that can provide a reference point for open loop control schemes. Multistable soft robotics takes advantage of both the adaptability of soft robotics and the potential for simplified control of multistable structures.</p> <p>Achieving simplified control for soft robotics is a necessary milestone in creating functional and applied soft robots. </p> <p>This work presents a means for simple open-loop control of a multistable soft robotic gripper that is adaptable, controllable, and robust. The behavior is illustrated through a gripper geometry described by specific design parameters resulting in a near infinite design space. An analytical model based on lumped parameter springs is derived, allowing us to search the design space in a tractable fashion. Specifically, we predict the system’s stable states for any given design instance by searching for local minima in the energy landscape formed by a spring lattice representation of our device. The lattice is composed of linear, bistable, and torsional springs—each of which contributes to the energy landscape of the system. We validate our model against Finite Element simulations of our device, showing good agreement with the proposed model. The aptitude of the model sheds light on the fundamental mechanics of our soft robotic gripper topology, laying the foundation for efficient design optimization and simplified control of soft robots.</p>

<p dir="ltr">Structural instability, traditionally deemed undesirable in engineering, can be leveraged for beneficial outcomes through intelligent design. One notable instance is elastic buckling, often leading to structures with two stable equilibria (bistable). Connecting bistable elements to form multistable mechanical metamaterials can enable the discretization and offer tunability of mechanical properties without the need for continuous energy input.<i> </i>In this work, we study the physics of these multistable metamaterials and utilize their state and property alterations along with snap-through instabilities resulting from state change for engineering applications. These materials hold potential for diverse applications, including mechanical and thermo-mechanical defrosting, energy absorption, energy harvesting, and mechanical storage and computation.</p><p dir="ltr">Focusing on defrosting, we find that the energy-efficient mechanical method using embedded bistable structures in heat exchanger fins significantly outperforms the thermal methods. The combination of manufacturing methods, material choice, boundary conditions, and actuation methodologies is systematically investigated to enhance defrosting performance. A purely mechanical strategy is effective against solid, glaze-like ice accumulations; however, performance is substantially diminished for low-density frost. To address this limitation, we study frost formation on the angular shape morphing fins and subsequently introduce a thermo-mechanical defrosting strategy. This hybrid approach focuses on the partial phase transition of low-density frost to solid ice through thermal methods, followed by mechanical defrosting. We experimentally validate this approach on a multistable heat exchanger fin pack.</p><p dir="ltr">Recent advancements have led to a new paradigm of reusable energy-absorbing materials, known as Phase Transforming Cellular Materials (PXCM) that utilize multiple negative stiffness elements connected in series. We explore the feasibility of this multistable metamaterial as frequency up-conversion material and utilize these phase transformations for energy harvesting. We experimentally demonstrate the energy-harvesting capabilities of a phase-transforming unit-cell-spring configuration and investigate the potential of multicell PXCM as an energy harvesting material.</p><p dir="ltr">The evolution towards intelligent matter, or physical intelligence, in the context of mechanical metamaterials can be characterized into four distinct stages: static, responsive, adaptive, and intelligent mechanical metamaterials. In the pursuit of designing intelligent mechanical metamaterials, there has been a resurgence in the field of mechanical computing. We utilize multistable metamaterials to develop mechanical storage systems that encode memory via bistable state changes and decode it through a global stiffness readout. We establish upper bounds for maximum memory capacity in elastic bit blocks and propose an optimal stiffness distribution for unique and identifiable global states. Through both parallel and series configurations, we realize various logic gates, thereby enabling in-memory computation. We further extend this framework by incorporating viscoelastic mechano-bits, which mimic the decay of neuronal action potentials. This allows for temporal stiffness modulation and results in increased memory storage via non-abelian behavior, for which we define a fundamental time limit of detectability. Additionally, we investigate information entropy in both elastic and viscoelastic systems, showing that temporal neural coding schemes can extend the system’s entropy beyond conventional limits. This is experimentally validated and shown to not only enhance memory storage but also augment computational capabilities.</p><p dir="ltr">The work in this thesis establishes multistability as a key design principle for developing responsive, adaptive, and intelligent materials, opening new avenues for future research in the field of multistable metamaterials.</p>