基于深度学习的微结构相关复合材料全场损伤和失效模式预测的数据驱动方法
A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning
摘要：
本文提出了一种基于图像的深度学习框架，用于预测微结构相关复合材料的损伤和失效。这项工作的动机是这种材料的高保真模拟的复杂性和计算成本。所提出的深度学习框架基于微观结构的几何形状预测复合材料的二维表示中的失效后全场应力分布和裂纹模式。感兴趣的材料被选择为高性能单向碳纤维增强聚合物复合材料。深度学习框架包含两个堆叠的全卷积网络，即Generator 1和Generator 2，按顺序训练。首先，发生器1学习将微观结构几何形状转换为全场失效后应力分布。然后，发电机2学会将发电机1的输出转换为故障模式。一个物理信息的损失函数也被设计和纳入，以进一步提高所提出的框架的性能，并促进验证过程。为了提供用于训练和验证深度学习框架的足够大的数据集，在高效的有限元框架中综合生成并模拟4500个微结构表示。结果表明，所提出的深度学习方法可以有效地预测复合材料的失效后全场应力分布和失效模式，这是计算固体力学中模拟的两个最复杂的现象。
英文摘要：
An image-based deep learning framework is developed in this paper to predict damage and failure in microstructure-dependent composite materials. The work is motivated by the complexity and computational cost of high-fidelity simulations of such materials. The proposed deep learning framework predicts the post-failure full-field stress distribution and crack pattern in two-dimensional representations of the composites based on the geometry of microstructures. The material of interest is selected to be a high-performance unidirectional carbon fiber-reinforced polymer composite. The deep learning framework contains two stacked fully-convolutional networks, namely, Generator 1 and Generator 2, trained sequentially. First, Generator 1 learns to translate the microstructural geometry to the full-field post-failure stress distribution. Then, Generator 2 learns to translate the output of Generator 1 to the failure pattern. A physics-informed loss function is also designed and incorporated to further improve the performance of the proposed framework and facilitate the validation process. In order to provide a sufficiently large data set for training and validating the deep learning framework, 4500 microstructural representations are synthetically generated and simulated in an efficient finite element framework. It is shown that the proposed deep learning approach can effectively predict the composites' post-failure full-field stress distribution and failure pattern, two of the most complex phenomena to simulate in computational solid mechanics.
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。
