Cellular Automata Inspired Multistable Origami Metamaterials for Mechanical Learning
Abstract: Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher‐level computation, existing computational frameworks require the integration of large‐scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing is proposed, which can accomplish high‐level computation tasks without the need to construct a logic gate network. This approach thus eliminates the demanding requirements for the fabrication of materials and signal propagation when constructing large‐scale networks for high‐level computation in conventional mechanical logic. Using the multistable stacked Miura‐origami metamaterial as a validation platform, digit recognition is experimentally implemented by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5‐bit memory tasks, are also shown to be feasible with the new computation framework. The research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials can have a transformative impact on a wide range of fields, from computational science to material mechano‐intelligence technology and beyond.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Cellular Automata Inspired Multistable Origami Metamaterials for Mechanical Learning ; day:23 ; month:10 ; year:2023 ; extent:11
Advanced science ; (23.10.2023) (gesamt 11)
- Creator
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Liu, Zuolin
Fang, Hongbin
Xu, Jian
Wang, Kon‐Well
- DOI
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10.1002/advs.202305146
- URN
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urn:nbn:de:101:1-2023102315365944217056
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:45 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Liu, Zuolin
- Fang, Hongbin
- Xu, Jian
- Wang, Kon‐Well
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