论文标题
使用神经网络探索薄壁的2D挤出晶格的结构 - 质地关系
Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks
论文作者
论文摘要
本文研究了动态纵向压缩下的薄壁晶格的结构特性关系，其特征在于它们的横截面和高度。这些关系阐明了设计的不同几何特征在机械响应上的相互作用，包括能量吸收。我们提出了一个基于密钥的组合设计系统，以生成不同的晶格设计，并使用有限元方法使用Johnson-Cook材料模型模拟其响应。使用自动编码器，我们将晶格的横截面图像编码为潜在的设计特征向量，并提供给神经网络模型以生成预测。受过训练的模型可以准确地预测基于密钥设计系统中的晶格能量吸收曲线，并可以通过转移学习扩展到系统以外的新设计。
This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression, characterized by their cross-sections and heights. These relations elucidate the interactions of different geometric features of a design on mechanical response, including energy absorption. We proposed a combinatorial, key-based design system to generate different lattice designs and used the finite element method to simulate their response with the Johnson-Cook material model. Using an autoencoder, we encoded the cross-sectional images of the lattices into latent design feature vectors, which were supplied to the neural network model to generate predictions. The trained models can accurately predict lattice energy absorption curves in the key-based design system and can be extended to new designs outside of the system via transfer learning.
