To reduce the cost of developing composite aeronautical structures, manufacturers and university researchers are increasingly using "virtual testing" methods. Then, finite element methods (FEMs) are intensively used to calculate mechanical behavior and to predict the damage to fiber-reinforced polymer (FRP) composites under impact loading, which is a crucial design aspect for aeronautical composite structures. But these FEMs require a lot of knowledge and a significant number of IT resources to run. Therefore, artificial intelligence could be an interesting way of sizing composites in terms of impact damage tolerance. In this research, the authors propose a methodology and deep learning-based approach to predict impact damage to composites. The data are both collected from the literature and created using an impact simulation performed using an FEM. The data augmentation method is also proposed to increase the data number from 149 to 2725. Firstly, a CNN model is built and optimized, and secondly, an aggregation of two CNN architectures is proposed. The results show that the use of an aggregation of two CNNs provides better performance than a single CNN. Finally, the aggregated CNN model prediction demonstrates the potential for CNN models to accelerate composite design by showing a 0.15 mm precision for all the length measurements, an average delaminated surface error of 56 mm2, and an error rate of 7% for the prediction of the presence of delamination.
Keywords: Convolutional Neural Network (CNN); carbon fiber-reinforced polymer; composite; impact; impact damage.