基于深度学习的短纤维增强聚氨酯复合材料性能预测

Taking advantages of deep learning in the field of image recognition, the convolutional neural network(CNN) was applied to construct a surrogate model to predict the macroscopic performance of the planar random short fiber reinforced urethane composites, and a data enhancement method was proposed to suppress overfitting occurred in the training process. The accuracy in tensile and shear properties of materials predicted by traditional and CNN surrogate models were compared. Results show that compared with the traditional method, CNN model is much better in learning the internal features of the image samples and obtains more accurate prediction results. Meanwhile, robustness is well maintained in a certain range outside the training sample space. Based on this, the proposed CNN model was combined with Monte Carlo method to study the forward propagation of error in the uncertainty of microgeometric parameters. The simulation result demonstrates that as the fiber aspect ratio increases, the uncertainties of the microgeometric parameters will lead to a nonnegligible error in the prediction of the effective properties of the material.