Adaptive Mechanical Metamaterials with On‐Demand Binary Local Modulus for Embodied Intelligence

Biological materials in nature are inherently adaptive, evolving through continuous interaction with their environment. Achieving such adaptability and self-optimization in artificial materials remains a major challenge. In this work, a simple yet robust mechanism is introduced that enables instantaneous changes in local stiffness components in response to strain. This is realized by designing binary meta-capsules with two discrete states 0 and 1, each corresponding to a different modulus in one direction. These strain-responsive capsules switch states based on applied deformation, serving as the building blocks for a new class of adaptive mechanical metamaterials (AMMs). Computational tools are developed to guide the design, and selected structures are fabricated via multi-material polymer jetting. Mechanical experiments, including compression and indentation tests, confirm the functionality of the AMMs. Because the stiffness change in each meta-capsule is reversible, the material can reconfigure itself after loading-unloading cycle. This enables AMMs to dynamically adjust their local properties based on external loads and/or constraints, effectively "reprogramming" or redesigning themselves post-fabrication, paving the way for transforming 3D/4D printing into adaptive, "infinity-D" printing.

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Publication Details

Journal
Advanced Science
Published
2025-08-26
DOI
https://doi.org/10.1002/advs.202509675
Citations
1
Primary Topic
Advanced Materials and Mechanics
Type
article
Field-Weighted Citation Impact
0.71
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article

Adaptive Mechanical Metamaterials with On‐Demand Binary Local Modulus for Embodied Intelligence

Yaning Li, Yunzheng Yang, Richard Nash
1 citations
Advanced Science
Advanced Materials and Mechanics
0.71
article

Adaptive Mechanical Metamaterials with On‐Demand Binary Local Modulus for Embodied Intelligence

Yaning Li, Yunzheng Yang, Richard Nash
article en
1 citations

Abstract

Biological materials in nature are inherently adaptive, evolving through continuous interaction with their environment. Achieving such adaptability and self-optimization in artificial materials remains a major challenge. In this work, a simple yet robust mechanism is introduced that enables instantaneous changes in local stiffness components in response to strain. This is realized by designing binary meta-capsules with two discrete states 0 and 1, each corresponding to a different modulus in one direction. These strain-responsive capsules switch states based on applied deformation, serving as the building blocks for a new class of adaptive mechanical metamaterials (AMMs). Computational tools are developed to guide the design, and selected structures are fabricated via multi-material polymer jetting. Mechanical experiments, including compression and indentation tests, confirm the functionality of the AMMs. Because the stiffness change in each meta-capsule is reversible, the material can reconfigure itself after loading-unloading cycle. This enables AMMs to dynamically adjust their local properties based on external loads and/or constraints, effectively "reprogramming" or redesigning themselves post-fabrication, paving the way for transforming 3D/4D printing into adaptive, "infinity-D" printing.

Advanced ScienceVol. 12(40)
Northeastern University (US)
Openalex Percentile: Top 28%
Advanced Materials and Mechanics
0.71
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Adaptive Mechanical Metamaterials with On‐Demand Binary Local Modulus for Embodied Intelligence — Yaning Li, Yunzheng Yang, et al. · Advanced Science (2025) | TGRS Research Map | TGRS