论文标题
基于微力学的复发性神经网络模型，用于路径依赖性循环变形，短纤维复合材料
A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites
论文作者
论文摘要
短纤维增强复合材料的宏观响应取决于广泛的微结构参数。因此，这些材料的微机械建模具有挑战性，在某些情况下，计算上很昂贵。当需要预测路径依赖的塑性行为时，这一点尤其重要。解决这一挑战的解决方案是通过机器学习技术（例如人工神经网络）增强微机械解决方案。在这项工作中，训练了一个经常性的深神经网络模型，以预测鉴于微结构参数和应变路径，短纤维增强复合材料的路径依赖性弹性塑料应力响应。进行了微机械均匀模拟，以创建一个数据库，以训练验证模型。与独立的微机械模拟相比，该模型以计算有效的方式提供了非常准确的预测。
The macroscopic response of short fiber reinforced composites is dependent on an extensive range of microstructural parameters. Thus, micromechanical modeling of these materials is challenging and in some cases, computationally expensive. This is particularly important when path-dependent plastic behavior is needed to be predicted. A solution to this challenge is to enhance micromechanical solutions with machine learning techniques such as artificial neural networks. In this work, a recurrent deep neural network model is trained to predict the path-dependent elasto-plastic stress response of short fiber reinforced composites, given the microstructural parameters and the strain path. Micromechanical meanfield simulations are conducted to create a data base for training the validating the model. The model gives very accurate predictions in a computationally efficient manner when compared with independent micromechanical simulations.
