发表机构
NSF Science and Technology Center for Engineering MechanoBiology; Washington University in St. Louis; Department of Mechanical Engineering & Materials Science; University of Cincinnati; New Jersey Institute of Technology; Washington University School of Medicine（NSF工程机械生物学科学与技术中心; 华盛顿大学圣路易斯分校; 机械工程与材料科学系; 辛辛那提大学; 新泽西理工学院; 华盛顿大学医学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究折纸超材料中拉伸与剪切耦合问题，利用工程无序实现更广泛力学响应区域，借助几何感知图神经网络和遗传算法设计图案，开发出无寄生剪切的拉伸结构材料。
AI 中文摘要
折纸能将硬片材转变为柔顺的、可变形的结构，但依赖周期性切割图案存在代价：相关面板旋转会将拉伸与剪切耦合，拉伸一个轴会驱动无法抑制的寄生剪切，且将各向异性刚度限制在狭窄、离散且无法独立调谐的响应集。生物组织通过可控无序克服类似限制。本文表明，工程无序是折纸的一个设计自由度，随机折纸能获得比周期性图案更连续、更广泛的力学响应区域，包括可编程各向异性及几乎完全消除拉伸 - 剪切耦合。因无序图案缺乏简单参数化，我们用几何感知图神经网络（GNN）和遗传算法在设计空间导航，GNN训练比基于图像的模型快且准。制造的弹性体样本重现了预测的非线性、各向异性响应。通过将无序变为控制方向刚度的变量，这项工作开发出无寄生剪切的拉伸结构材料，从软致动器到与活组织各向异性匹配的组织接口设备。
英文摘要
Kirigami turns stiff sheets into compliant, shape-morphing structures, but its reliance on periodic cut patterns comes at a cost: correlated panel rotations couple extension to shear, so stretching one axis drives a parasitic shear that cannot be suppressed, and also confine anisotropic stiffness to a narrow, discrete set of responses that cannot be tuned independently. Biological tissues overcome an analogous constraint through controlled disorder, such as graded fiber orientations in skin and hierarchical anisotropy in myocardium, achieving direction-dependent mechanics unavailable to regular architectures. Here, we show that engineered disorder is a design degree of freedom for kirigami, with stochastic kirigami accessing a continuous and far broader region of mechanical response than periodic patterns. This includes programmable anisotropy with near-complete elimination of extension-shear coupling. Because disordered patterns lack a simple parameterization, we navigate this design space with a geometry-aware graph neural network (GNN) that maps cut topology to the full nonlinear, bidirectional stress-strain response, coupled to a genetic algorithm that inverse-designs patterns reproducing target responses along two perpendicular axes. The GNN trains an order of magnitude faster and more accurately than image-based models. Fabricated elastomer samples reproduce the predicted nonlinear, anisotropic responses, closing the loop from design to physical component. By turning disorder into a variable to control directional stiffness, this work develops architected materials that stretch without parasitic shear, from soft actuators to tissue-interfacing devices matched to the anisotropy of living tissue.
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