材料科学
反向
超材料
复合材料
拉伤
机械设计
应力-应变曲线
压力(语言学)
机械工程
变形(气象学)
光电子学
几何学
医学
语言学
哲学
数学
内科学
工程类
作者
Zhiping Chai,Zisheng Zong,Haochen Yong,Xingxing Ke,Jiaqi Zhu,Han Ding,Chuan Fei Guo,Zhigang Wu
标识
DOI:10.1002/adma.202404369
摘要
Abstract By incorporating soft materials into the architecture, flexible mechanical metamaterials enable promising applications, e.g., energy modulation, and shape morphing, with a well‐controllable mechanical response, but suffer from spatial and temporal programmability towards higher‐level mechanical intelligence. One feasible solution is to introduce snapping structures and then tune their responses by accurately tailoring the stress–strain curves. However, owing to the strongly coupled nonlinearity of structural deformation and material constitutive model, it is difficult to deduce their stress–strain curves using conventional ways. Here, a machine learning pipeline is trained with the finite element analysis data that considers those strongly coupled nonlinearities to accurately tailor the stress–strain curves of snapping metamaterialfor on‐demand mechanical response with an accuracy of 97.41%, conforming well to experiment. Utilizing the established approach, the energy absorption efficiency of the snapping‐metamaterial‐based device can be tuned within the accessible range to realize different rebound heights of a falling ball, and soft actuators can be spatially and temporally programmed to achieve synchronous and sequential actuation with a single energy input. Purely relying on structure designs, the accurately tailored metamaterials increase the devices’ tunability/programmability. Such an approach can potentially extend to similar nonlinear scenarios towards predictable or intelligent mechanical responses.
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