通过深度生成模型控制曲梁超材料中的拉胀性
Controlling auxeticity in curved-beam metamaterials via a deep generative model
AI总结：
本文提出用曲梁替代直梁以在不改变单元胞尺寸的情况下控制凹角六边形超材料的泊松比，并引入结合引导结构和目标性能的深度生成模型，实现具有特定力学性能的曲梁超材料单元胞的逆向设计与准确生成。
摘要：
基于晶格的力学超材料因其合理的内部结构而表现出相当独特的力学行为。这包括诸如负泊松比等不寻常的性质，在凹角六边形超材料中可以通过调整梁之间的角度轻松调节。然而，改变角度也会影响单元胞的整体尺寸。我们表明，通过用曲梁替代传统的直梁，可以在不影响凹角六边形超材料整体尺寸的情况下控制其泊松比。虽然这些结构的力学性能可以通过有限元模拟或在某些情况下通过解析方法预测，但许多应用需要识别具有特定目标性能的结构。为了解决这一逆问题，我们引入了一个深度学习框架，用于生成具有所需性能的超材料。通过向生成模型提供引导结构以及目标性能，我们不仅能够生成大量具有相同性能的替代结构，还能表达对特定形状的偏好。深度学习预测与实验测量共同证明，该方法使我们能够准确生成符合曲梁超材料特定性能的单元胞。
英文摘要：
Lattice-based mechanical metamaterials are known to exhibit quite a unique mechanical behavior owing to their rational internal architecture. This includes unusual properties such as a negative Poisson's ratio, which can be easily tuned in reentrant-hexagonal metamaterials by adjusting the angles between beams. However, changing the angles also affects the overall dimensions of the unit cell. We show that by replacing traditional straight beams with curved ones, it is possible to control Poisson's ratio of reentrant-hexagonal metamaterials without affecting their overall dimensions. While the mechanical properties of these structures can be predicted through finite element simulations or, in some cases, analytically, many applications require to identify architectures with specific target properties. To solve this inverse problem, we introduce a deep learning framework for generating metamaterials with desired properties. By supplying the generative model with a guide structure in addition to the target properties, we are not only able to generate a large number of alternative architectures with the same properties, but also to express preference for a specific shape. Deep learning predictions together with experimental measurements prove that this approach allows us to accurately generate unit cells fitting specific properties for curved-beam metamaterials.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
