Reprogrammable flexible mechanical metamaterials

Zheng, Xiaoyang; Uto, Koichiro; Hu, Wei‐Hsun; Chen, Ta‐Te; Naito, Masanobu; Watanabe, Ikumu

doi:10.1016/j.apmt.2022.101662

Cited by 16 publications

(11 citation statements)

References 76 publications

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“…The seeds were generated in unit cells and the center unit cell was defined as an RVE. The same approach was employed for 2D design in our previous study [ 17 ]. The Voronoi skeleton was derived from the polyhedral meshes of the Voronoi diagram.…”

Section: Methodsmentioning

confidence: 99%

“…More importantly, recent advances in additive manufacturing have enabled the fabrication of complicated structures using multimaterials [ 18 , 19 ], shape memory polymers [ 20–22 ], and magnetic materials [ 23 , 24 ]. Thus, the rational design of architected cellular materials makes them promising candidates for soft robotics [ 18 , 23–25 ], actuators [ 16 , 17 , 26 ], soft electronics [ 27 , 28 ], tissue engineering [ 29–32 ], and electrochemical energy storage and conversion [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

Zheng

Chen

Jiang

et al. 2023

Science and Technology of Advanced Materials

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Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young’s moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young’s modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young’s modulus.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

Zheng

Chen

Jiang

et al. 2023

Science and Technology of Advanced Materials

Self Cite

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“…Once manufactured, however, their as-built architecture is permanently imprinted with snapthrough characteristics that cannot be changed in service through a mechanical input unless a field-responsive material is used and triggered by the application of an external physical field, such as a thermal or electromagnetic stimulus. [21,33,34] As a result, current concepts that do not rely on a physical stimulus cannot switch their mechanical response in situ, for example from snap-through to monotonic, because their snap-thorough instability cannot be intrinsically deactivated post-fabrication. This immutability prevents them from behaving as conventional materials delivering a monotonic stress-strain relation, and from using them in a multitude-even larger-spectrum of ordinary applications where snapping instabilities are detrimental.…”

Section: Introductionmentioning

confidence: 99%

In Situ Activation of Snap‐Through Instability in Multi‐Response Metamaterials through Multistable Topological Transformation

Pasini

2023

Advanced Materials

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Snap‐through instability has been widely leveraged in metamaterials to attain non‐monotonic responses for a specific subset of applications where conventional monotonic materials fail to perform. In the remaining more plentiful set of ordinary applications, snap‐through instability is harmful, and current snapping metamaterials become inadequate because their capacity to snap cannot be suppressed post‐fabrication. Here, a class of topology‐transformable metamaterials is introduced to enable in situ activation and deactivation of the snapping capacity, providing a remarkable level of versatility in switching between responses from monotonic to monostable and bistable snap‐through. Theoretical analysis, numerical simulations, and experiments are combined to unveil the role played by contact in the topological transformation capable of increasing the geometry incompatibility and confinement stiffness of selected architectural members. The strategy here presented for post‐fabrication reprogrammability of matter and on‐the‐fly response switching paves the way to multifunctionality for application in multiple sectors from mechanical logic gates, and adjustable energy dissipators, to in situ adaptable sport equipment.

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“…We aim to establish and harness a one-to-one mapping between temperature and nonlinear mechanical behavior through the metamaterial. In the literature, a widely adopted approach to achieve temperature-dependent behaviors is using shape memory materials that actuate and alter mechanical behaviors (18)(19)(20)(21)(22)(23)(24)(25), such as deformation modes (20) and effective properties (19). However, the accompanied shape changes in the unloaded states may not suit applications requiring functional changes, such as force-displacement (FD) responses dissociated from shape changes and free of external power or intervention.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic encoding of adaptive responses in temperature-sensing multimaterial architectures

Li,

Wang,

Chen

et al. 2023

Sci. Adv.

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We envision programmable matters that can alter their physical properties in desirable manners based on user input or autonomous sensing. This vision motivates the pursuit of mechanical metamaterials that interact with the environment in a programmable fashion. However, this has not been systematically achieved for soft metamaterials because of the highly nonlinear deformation and underdevelopment of rational design strategies. Here, we use computational morphogenesis and multimaterial polymer 3D printing to systematically create soft metamaterials with arbitrarily programmable temperature-switchable nonlinear mechanical responses under large deformations. This is made possible by harnessing the distinct glass transition temperatures of different polymers, which, when optimally synthesized, produce local and giant stiffness changes in a controllable manner. Featuring complex geometries, the generated structures and metamaterials exhibit fundamentally different yet programmable nonlinear force-displacement relations and deformation patterns as temperature varies. The rational design and fabrication establish an objective-oriented synthesis of metamaterials with freely tunable thermally adaptive behaviors. This imbues structures and materials with environment-aware intelligence.

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Reprogrammable flexible mechanical metamaterials

Cited by 16 publications

References 76 publications

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

In Situ Activation of Snap‐Through Instability in Multi‐Response Metamaterials through Multistable Topological Transformation

Algorithmic encoding of adaptive responses in temperature-sensing multimaterial architectures

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