Inverse design of magneto-active metasurfaces and robots: Theory, computation, and experimental validation

Wang, Chao; Zhao, Zhi; Zhang, Xiaojia Shelly

doi:10.1016/j.cma.2023.116065

Cited by 12 publications

(1 citation statement)

References 61 publications

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“…(2) Secondly, achieving morphing into intricate 3D geometries poses a challenging inverse-design problem [116,165] . While analytical models can be developed for specific strain-driven morphing scenarios, they encounter difficulties when confronted with complex shapes that involve intricate interactions between actuating forces and nonlinear material deformations.…”

Section: Discussionmentioning

confidence: 99%

Morphing matter: from mechanical principles to robotic applications

Yang,

Zhou,

Zhao

et al. 2023

Soft Sci

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The adaptability of natural organisms in altering body shapes in response to the environment has inspired the development of artificial morphing matter. These materials encode the ability to transform their geometrical configurations in response to specific stimuli and have diverse applications in soft robotics, wearable electronics, and biomedical devices. However, achieving the morphing of intricate three-dimensional shapes from a two-dimensional flat state is challenging, as it requires manipulations of surface curvature in a controlled manner. In this review, we first summarize the mechanical principles extensively explored for realizing morphing matter, both at the material and structural levels. We then highlight its applications in the soft robotics field. Moreover, we offer insights into the open challenges and opportunities that this rapidly growing field faces. This review aims to inspire researchers to uncover innovative working principles and create multifunctional morphing matter for various engineering fields.

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Section: Discussionmentioning

confidence: 99%

Morphing matter: from mechanical principles to robotic applications

Yang,

Zhou,

Zhao

et al. 2023

Soft Sci

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Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials Under Heterogeneous Fields

Lee,

Zhang,

et al. 2024

Advanced Optical Materials

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Multifunctional metamaterials (MMM) bear promise as next‐generation material platforms supporting miniaturization and customization. Despite many proof‐of‐concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta‐atom coupling or long‐range interactions, remain largely under‐explored. To this end, a data‐driven design framework is presented, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions. Additionally, a standard formulation of inverse problem covering a broad class of MMMs is presented, together with gradient‐based multitask concurrent optimization identifying a set of Pareto‐optimal architecture‐stimulus (A‐S) pairs. Fourier multiclass blending is proposed to synthesize inter‐class meta‐atoms anchored on a set of geometric motifs, while enjoying training‐free dimension reduction and built‐it reconstruction. Interlocking the three pillars, the framework is validated for light‐by‐light programmable nanoantenna, whose design involves vast space jointly spanned by quasi‐freeform supercells, maneuverable incident phase distributions, and conflicting figure‐of‐merits (FoM) involving on‐demand localization patterns. Accommodating all the challenges, the framework can propel future advancements of MMM.

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Wireless Magnetic Robot for Precise Hierarchical Control of Tissue Deformation

Wang,

Zhao,

Han

et al. 2024

Advanced Science

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Mechanotherapy has emerged as a promising treatment for tissue injury. However, existing robots for mechanotherapy are often designed on intuition, lack remote and wireless control, and have limited motion modes. Herein, through topology optimization and hybrid fabrication, wireless magneto‐active soft robots are created that can achieve various modes of programmatic deformations under remote magnetic actuation and apply mechanical forces to tissues in a precise and predictable manner. These soft robots can quickly and wirelessly deform under magnetic actuation and are able to deliver compressing, stretching, shearing, and multimodal forces to the surrounding tissues. The design framework considers the hierarchical tissue‐robot interaction and, therefore, can design customized soft robots for different types of tissues with varied mechanical properties. It is shown that these customized robots with different programmable motions can induce precise deformations of porcine muscle, liver, and heart tissues with excellent durability. The soft robots, the underlying design principles, and the fabrication approach provide a new avenue for developing next‐generation mechanotherapy.

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Inverse design of magneto-active metasurfaces and robots: Theory, computation, and experimental validation

Cited by 12 publications

References 61 publications

Morphing matter: from mechanical principles to robotic applications

Morphing matter: from mechanical principles to robotic applications

Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials Under Heterogeneous Fields

Wireless Magnetic Robot for Precise Hierarchical Control of Tissue Deformation

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