Taming geometric frustration by leveraging structural elasticity

Udani, Janav P.; Arrieta, Andres F.

doi:10.1016/j.matdes.2022.110809

Cited by 11 publications

(8 citation statements)

References 53 publications

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“…The mechanism underlying the non-Abelian response sheds light on the generic emergence of sequence-dependent responses in frustrated mechanical systems 25 , 26 , 52 – 54 . The order in which bistable degrees of freedom are manipulated follows a pathway of states, which favors one of the possible final states.…”

Section: Discussionmentioning

confidence: 99%

Emergent disorder and mechanical memory in periodic metamaterials

Sirote-Katz,

Shohat,

Merrigan

et al. 2024

Nat Commun

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Ordered mechanical systems typically have one or only a few stable rest configurations, and hence are not considered useful for encoding memory. Multistable and history-dependent responses usually emerge from quenched disorder, for example in amorphous solids or crumpled sheets. In contrast, due to geometric frustration, periodic magnetic systems can create their own disorder and espouse an extensive manifold of quasi-degenerate configurations. Inspired by the topological structure of frustrated artificial spin ices, we introduce an approach to design ordered, periodic mechanical metamaterials that exhibit an extensive set of spatially disordered states. While our design exploits the correspondence between frustration in magnetism and incompatibility in meta-mechanics, our mechanical systems encompass continuous degrees of freedom, and thus generalize their magnetic counterparts. We show how such systems exhibit non-Abelian and history-dependent responses, as their state can depend on the order in which external manipulations were applied. We demonstrate how this richness of the dynamics enables to recognize, from a static measurement of the final state, the sequence of operations that an extended system underwent. Thus, multistability and potential to perform computation emerge from geometric frustration in ordered mechanical lattices that create their own disorder.

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Section: Discussionmentioning

confidence: 99%

Emergent disorder and mechanical memory in periodic metamaterials

Sirote-Katz,

Shohat,

Merrigan

et al. 2024

Nat Commun

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“…Thus, the electrical resistance of the piezoresistive sensor,

R_{ε}

, and the structural behavior of the dome are intrinsically linked. Unlike previous research that leveraged the hierarchical behaviors of metasheets with closely spaced domes, [ 22,23 ] we use sufficient spacing, w , in the metasheet to ensure that neighboring unit cells do not significantly affect each other. Thus, characterization studies can be performed on individual unit cells.…”

Section: Mechanosensingmentioning

confidence: 99%

“…These simulations reveal that the stresses and strains in the membrane are inversely proportional to the radial distance from the center of the dome, r (Figure 2b, S1b, Supporting Information). This relationship between r and the radial and tangential stresses,

σ_{normalr}

and

σ_{θ}

, follow the following analytical expressions, where

D_{2}

D_{3}

, and

D_{4}

are constants [ 23 ]

σ_{normalr} = D_{2} (1 / r^{2}) + D_{3} + D_{4} \log (r) + \dots

σ_{θ} = - D_{2} (1 / r^{2}) + D_{3} + D_{4} (1 + \log (r)) + \dots

…”

Section: Mechanosensingmentioning

confidence: 99%

“…The spatial resolution of the metasheet can be made finer or coarser by scaling the unit cells' local geometry and spacing. [22,23] Unlike previous sensing skin devices aiming for high sensitivity over a small area, similar to a fingertip, [34,35] we target large area applications, such as pressure sensing over an aircraft wing (Figure 1b). Such large-scale applications further underscore the necessity of filtering and nonlinear amplification to avoid data overload.…”

Section: Spatiotemporal Memorymentioning

confidence: 99%

“…10 2 cm 2 ) at tunable sensitivity and resolution levels (Figure 1a.i). [22][23][24][25] We encode the transduced signals by firing nonvolatile memristor adaptation, thus providing a means to store spatially distributed mechanical signals in analog material states (Figure 1a.ii).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning

Riley

Koner

Osorio-Pinzon

et al. 2022

Advanced Intelligent Systems

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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.

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Anisotropic Morphing in Bistable Kirigami through Symmetry Breaking and Geometric Frustration

Qiao,

Agnelli,

Pokkalla

et al. 2024

Advanced Materials

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Shape morphing in bistable kirigami enables remarkable functionalities appealing to a diverse range of applications across the spectrum of length scale. At the core of their shape shifting lies the architecture of their repeating unit, where highly deformable slits and quasi‐rigid rotating units often exhibit multiple symmetries that confers isotropic deployment obeying uniform scaling transformation. In this work, symmetry breaking in bistable kirigami is investigated to access geometric frustration and anisotropic morphing, enabling arbitrarily scaled deployment in planar and spatial bistable domains. With an analysis on their symmetry properties complemented by a systematic investigation integrating semi‐analytical derivations, numerical simulations, and experiments on elastic kirigami sheets, this work unveils the fundamental relations between slit symmetry, geometric frustration, and anisotropic bistable deployment. Furthermore, asymmetric kirigami units are leveraged in planar and flat‐to‐3D demonstrations to showcase the pivotal role of shear deformation in achieving target shapes and functions so far unattainable with uniformly stretchable kirigami. The insights provided in this work unveil the role of slit symmetry breaking in controlling the anisotropic bistable deployment of soft kirigami metamaterials, enriching the range of achievable functionalities for applications spanning deployable space structures, wearable technologies, and soft machines.This article is protected by copyright. All rights reserved

show abstract

Taming geometric frustration by leveraging structural elasticity

Cited by 11 publications

References 53 publications

Emergent disorder and mechanical memory in periodic metamaterials

Emergent disorder and mechanical memory in periodic metamaterials

Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning

Anisotropic Morphing in Bistable Kirigami through Symmetry Breaking and Geometric Frustration

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