基于kirigami截集和主动学习的全柔性可展开结构快速设计
Rapid design of fully soft deployable structures via kirigami cuts and active learning
摘要：
与传统的基于铰链和弹簧的分段刚性可展开结构不同，软可展开结构可以呈现复杂的3D形状，从而实现软机器人，形状变形架构和弹出式制造中的变革技术。它们几乎无限的自由度允许对最终形状进行精确控制。然而，相同的实现高维度，对解决涉及这类结构的逆设计问题提出了挑战：为了实现所需的3D结构，通常需要在制造期间具有广泛的局部致动和控制的制造技术，以及在大的设计空间上的试错搜索。我们解决这两个缺点，首先开发一个简化的平面制造方法，结合了两个成分：两层复合材料壳和kirigami削减，缓解局部应力之间的应变不匹配。原则上，可以通过设计适当的kirigami切割和选择正确的预拉伸量来生成目标3D形状，从而消除对局部控制的需要。其次，我们制定了一个数据驱动的物理指导的框架，降低了使用自动编码器的逆设计问题的维数，并有效地搜索通过“潜在的”参数空间中的主动学习方法。我们通过一系列的目标形状，如花生，品客薯片，花，金字塔证明了快速设计过程的有效性。进行了桌面实验来制作目标形状。实验结果和数值预测，从我们的框架被发现是在良好的协议。
英文摘要：
Soft deployable structures - unlike conventional piecewise rigid deployables based on hinges and springs - can assume intricate 3-D shapes, thereby enabling transformative technologies in soft robotics, shape-morphing architecture, and pop-up manufacturing. Their virtually infinite degrees of freedom allow precise control over the final shape. The same enabling high dimensionality, however, poses a challenge for solving the inverse design problem involving this class of structures: to achieve desired 3D structures it typically requires manufacturing technologies with extensive local actuation and control during fabrication, and a trial and error search over a large design space. We address both of these shortcomings by first developing a simplified planar fabrication approach that combines two ingredients: strain mismatch between two layers of a composite shell and kirigami cuts that relieves localized stress. In principle, it is possible to generate targeted 3-D shapes by designing the appropriate kirigami cuts and selecting the right amount of prestretch, thereby eliminating the need for local control. Second, we formulate a data-driven physics-guided framework that reduces the dimensionality of the inverse design problem using autoencoders and efficiently searches through the ``latent" parameter space in an active learning approach. We demonstrate the effectiveness of the rapid design procedure via a range of target shapes, such as peanuts, pringles, flowers, and pyramids. Tabletop experiments are conducted to fabricate the target shapes. Experimental results and numerical predictions from our framework are found to be in good agreement.
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