Spring-based mechanical metamaterials with deep-learning-accelerated design

Xiaofeng Guo; Xiaoyang Zheng; Jiaxin Zhou; Takayuki Yamada; Yong Yi; Ikumu Watanabe

doi:10.1016/j.matdes.2025.113800

論文 Spring-based mechanical metamaterials with deep-learning-accelerated design

Xiaofeng Guo ; Xiaoyang Zheng ; Jiaxin Zhou ; Takayuki Yamada ; Yong Yi ; Ikumu Watanabe

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引用

Xiaofeng Guo, Xiaoyang Zheng, Jiaxin Zhou, Takayuki Yamada, Yong Yi, Ikumu Watanabe. Spring-based mechanical metamaterials with deep-learning-accelerated design. Materials & Design. 2025, 252 (), 113800. https://doi.org/10.1016/j.matdes.2025.113800

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(abstract)

Mechanical metamaterials exhibit unique properties that depend on their microstructure and surpass those of their constituent materials. Flexible mechanical metamaterials, in particular, hold significant potential for applications requiring substantial deformations, such as soft robotics and energy absorption. In this study, we proposed a collection of flexible mechanical metamaterials discretely assembled using structural spring elements. These spring elements enhance both flexibility and reversibility, allowing the materials to withstand large deformations. The geometric regularity of the metamaterials enables zero-shot learning, allowing deep learning frameworks to address property prediction and inverse design problems beyond the training dataset.
Using a property-prediction model, the effective mechanical properties of these metamaterials can be accurately predicted based on specified design parameters. Furthermore, an inverse-design model enables the direct generation of mechanical metamaterials with desired target properties, even outside the training dataspace, in the range of Young's modulus in (0, 350) kPa and Poisson's ratio in (-0.12, 0.12). The properties of these inversely designed metamaterials are analyzed through finite element method simulations and mechanical testing. The deep learning-accelerated design approach not only streamlines the development process but also provides a framework for advancing metamaterial design, encompassing property prediction and inverse design.

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