A New Method to Predict Damage to Composite Structures Using Convolutional Neural Networks

Abstract

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.

Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction

“…As computationally expensive simulations are used to generate the data for training and validating data-driven models, in real-world applications, it is desirable to construct ML models using minimal data. Nevertheless, it is widely acknowledged that an insufficient training data volume can lead to inaccurate model predictions [13,21,38]. While an illrepresentation of the actual process is unacceptable, performing excessive simulations for model training also contradicts the intended purpose of cost reduction [1,26,38].…”

“…Nevertheless, it is widely acknowledged that an insufficient training data volume can lead to inaccurate model predictions [13,21,38]. While an illrepresentation of the actual process is unacceptable, performing excessive simulations for model training also contradicts the intended purpose of cost reduction [1,26,38]. Therefore, investigating and potentially optimising the cost-accuracy trade-off of ML approaches is of great research interest and value.…”

“…Highlighted across various reviews and recent papers, the adoption of ML approaches in composite manufacturing applications is still in its early stages and requires further development [16,36,37]. Currently, most relevant studies in the literature have focused on utilising machine learning to predict composite material properties [29,32,36] or to detect composite damage/delamination [34,37,38]. In contrast, contemporary applications of ML approaches in RTM process modelling and optimisation are still lacking or are rudimentary [13,16,26].…”