Predicting the Non-linear Response of Composite Materials using Deep Recurrent Convolutional Neural Networks
Predicting the Non-linear Response of Composite Materials using Deep Recurrent Convolutional Neural Networks
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DOI：
10.1016/j.ijsolstr.2023.112334
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发表时间：
2023-05
影响因子：
3.6
通讯作者：
Bassam El Said
中科院分区：
文献类型：
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作者：
Bassam El Said
本文提出了一种新的框架，使用深度递归卷积神经网络（DCRN）预测复合材料的全非线性响应。该框架是基于一个代表性体积元素（RVE）数据库填充复合材料设计空间的铺层，缺陷/变异性和加载条件的采样。在这些模型中包括了材料非线性的几个来源，如基体损伤，分层，纤维失效和剪切非线性。提出了一种DCRN网络架构，该架构将用于空间特征检测的卷积层与用于材料加载历史依赖性的长期/短期记忆层相结合。模型的数据库由每个RVE模型的图像组成，代表铺层和变量，如是否存在褶皱、间隙或空隙，以及均匀的3D应力/应变曲线。DCRN网络使用来自RVE模型数据库的信息进行训练，以预测层压复合材料的全三维应力响应。两个配方的DCRN网络进行了研究，逐点预测配方和时间推进预测配方。基于准确度和耐用性比较两种制剂。结果表明，这两种方法都能准确地预测复合材料层合板的非线性响应。
This paper presents a novel framework to predict the full non-linear response of composite materials using Deep Recurrent Convolutional Neural (DCRN) Networks. The framework is based on a Representative Volume Element (RVE) database populated by sampling the composite design space in terms of layups, defects/variabilities and loading conditions. Several sources of material non-linearity are included in these models such as matrix damage, delamination, fibre failure and shear non-linearity. A DCRN Network architecture is proposed which combines convolutional layers, for spatial features detection, with Long/Short Term Memory layers, for material loading history dependencies. The models’ database consists of images of each RVE model, representing the layup and variables such as the presence of wrinkles, gaps or voids, and the homogenised 3D stress/strain curves. DCRN Networks are trained to predict the full 3D stress response of a laminated composite using the information from the RVE models database. Two formulations for the DCRN Network are studied, a pointwise prediction formulation and a time-marching prediction formulation. The two formulations are compared based on accuracy and robustness. The results show that both approaches can accurately predict the non-linear response of laminated composites.