Despite advancements in predicting the constitutive relationships of composite materials, characterizing the effects of microstructural randomness on their mechanical behaviors remains challenging. In this study, we propose a data-driven convolutional neural network (CNN) to efficiently predict the stress-strain curves containing three key material features (Tensile strength, modulus, and toughness) of fiber reinforced composites. Firstly, stress-strain curves for composites with arbitrary fiber distributions were generated using experimentally validated peridynamics (PD) model. Principal component analysis (PCA) was then employed to learn these curves in a lower-dimensional space, reducing computational costs. Subsequently, these reduced data, along with randomly distributed microstructural features, were used to train, validate, and evaluate the CNN models. The combined CNN and PCA model accurately predicted stress-strain curves with maximum errors of 2.5 % for tensile strength, 10% for modulus, and 20 % for toughness. Furthermore, data augmentation and Mean Squared Error (MSE) as a loss function significantly enhanced the model's prediction accuracy. Our findings indicated that DenseNet121 outperformed other CNN models in predicting the properties of fiber-reinforced materials, further demonstrating the effectiveness of the proposed model. This work successfully demonstrates the applicability of a data-driven CNN approach to predict stress-strain relations for engineering materials with intricate heterogeneous microstructures, paving the way for data-driven computational mechanics applied in composites.

中文翻译：

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用于预测纤维增强复合材料力学本构行为的近动力学驱动卷积神经网络


尽管在预测复合材料的本构关系方面取得了进展，但表征微观结构随机性对其机械行为的影响仍然具有挑战性。在这项研究中，我们提出了一个数据驱动的卷积神经网络 （CNN），以有效预测包含纤维增强复合材料的三个关键材料特征（拉伸强度、模量和韧性）的应力-应变曲线。首先，使用经过实验验证的近场动力学 （PD） 模型生成具有任意纤维分布的复合材料的应力-应变曲线。然后采用主成分分析 （PCA） 在低维空间中学习这些曲线，从而降低计算成本。随后，这些减少的数据以及随机分布的微观结构特征被用于训练、验证和评估 CNN 模型。CNN 和 PCA 组合模型准确预测了应力-应变曲线，拉伸强度的最大误差为 2.5%，模量的最大误差为 10%，韧性的最大误差为 20%。此外，数据增强和均方误差 （MSE） 作为损失函数显着提高了模型的预测准确性。我们的研究结果表明，DenseNet121 在预测纤维增强材料的性能方面优于其他 CNN 模型，进一步证明了所提出的模型的有效性。这项工作成功地证明了数据驱动的 CNN 方法在预测具有复杂异质微结构的工程材料的应力-应变关系方面的适用性，为数据驱动的计算力学应用于复合材料铺平了道路。