变形
非线性系统
材料科学
超材料
人工神经网络
机械系统
四边形的
机器人
航程(航空)
反向
微电子机械系统
机械能
设计策略
计算机科学
机械工程
结构工程
人工智能
纳米技术
物理
工程类
有限元法
光电子学
数学
几何学
复合材料
功率(物理)
量子力学
作者
Bolei Deng,Ahmad Zareei,Xiaoxiao Ding,James C. Weaver,Chris H. Rycroft,Katia Bertoldi
标识
DOI:10.1002/adma.202206238
摘要
Materials with target nonlinear mechanical response can support the design of innovative soft robots, wearable devices, footwear, and energy-absorbing systems, yet it is challenging to realize them. Here, mechanical metamaterials based on hinged quadrilaterals are used as a platform to realize target nonlinear mechanical responses. It is first shown that by changing the shape of the quadrilaterals, the amount of internal rotations induced by the applied compression can be tuned, and a wide range of mechanical responses is achieved. Next, a neural network is introduced that provides a computationally inexpensive relationship between the parameters describing the geometry and the corresponding stress-strain response. Finally, it is shown that by combining the neural network with an evolution strategy, one can efficiently identify geometries resulting in a wide range of target nonlinear mechanical responses and design optimized energy-absorbing systems, soft robots, and morphing structures.
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