There are a wide variety of microstructural parameters which affect the macro-mechanical response of short fiber reinforced composites. Effects of these parameters could be captured using different micromechanics-based models. However, in some cases, it is very challenging and computationally expensive. In this study, a micromechanics-based Artificial Neural Networks (ANN) model is developed to predict the elastic properties of these materials, accurately and quickly. The required data for training and validating the model is created using a two-step approach, combining Finite Element Analysis and Orientation Averaging. The capability of the model for fair predictions is proven, not only by using the validation data, but also by comparisons to experimental results taken from literature.

有各种各样的微观结构参数会影响短纤维增强复合材料的宏观力学响应。可以使用不同的基于微力学的模型来捕获这些参数的影响。但是，在某些情况下，这是非常具有挑战性的，并且计算量很大。在这项研究中，开发了一种基于微力学的人工神经网络（ANN）模型，以准确，快速地预测这些材料的弹性特性。训练和验证模型所需的数据使用两步法创建，结合了有限元分析和方向平均。不仅通过使用验证数据，而且通过与从文献中获得的实验结果进行比较，证明了该模型具有合理预测的能力。