This paper describes a data-driven approach to predict mechanical properties of auxetic kirigami metamaterials with randomly oriented cuts. The finite element method (FEM) was used to generate datasets, the convolutional neural network (CNN) was introduced to train these data, and an implicit mapping between the input orientations of cuts and the output Young’s modulus and Poisson’s ratio of the kirigami sheets was established. With this input–output relationship in hand, a quick estimation of auxetic behavior of kirigami metamaterials is straightforward. Our examples indicate that if the distributions of training and test datasets are close to each other, a good prediction is achievable. Our efforts provide a fast and reliable way to evaluate the homogenized properties of mechanical metamaterials with various microstructures, and thus accelerate the design of mechanical metamaterials for diverse applications.

中文翻译：

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一种预测性的深度学习方法，用于随机定向切割的拉胀剪纸超材料的均质化

本文描述了一种数据驱动的方法来预测具有随机定向切割的拉胀剪纸超材料的机械性能。使用有限元方法（FEM）生成数据集，引入卷积神经网络（CNN）来训练这些数据，并且切割的输入方向与剪纸片的输出杨氏模量和泊松比之间的隐式映射是已确立的。有了这种输入-输出关系，对剪纸超材料的拉胀行为的快速估计就很简单了。我们的示例表明，如果训练和测试数据集的分布彼此接近，则可以实现良好的预测。我们的努力提供了一种快速可靠的方法来评估具有各种微观结构的机械超材料的均质化性能，