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.

In aeronautics, honeycomb sandwich structures are widely used for secondary structures such as landing gear doors, flaps or floors, and for primary structures in helicopters or business jets. These structures are generally joined by using local reinforcements of the insert type. In the present study, 50 J low velocity impact tests were performed on inserts using a drop-weight device and the impact response and failure patterns were analysed. Impacted specimens were then pull-through tested to failure. Some of the tests were stopped before final failure in order to obtain precise details on the failure scenario. It was shown that, in the cases studied, the residual strength after impact was very high (about 90%) in comparison to the large reductions habitually observed in compression after impact tests.

The use of composites in the aerospace industry has been increasing exponentially. However, conventional epoxy resins, derived from petroleum sources, are not sustainable, making them non-degradable and environmentally harmful. In order to foster a sustainable environment, replacing conventional thermoset epoxies with bio-sourced carbon epoxies is imperative. With the enhancement in technology, it is possible to combine vegetable oils or bio-based copolymers with resins to make it recyclable in nature. Hence, it is necessary to study bio-based epoxies and carry out material characterization and see how they behave differently from conventional epoxies. This study examines the mechanical properties of different types of epoxy resins, which includes conventional, recyclable, and non-recyclable bio-epoxies. Tensile, bending, fracture toughness, and compression tests are performed in accordance with ASTM and ISO standards. The results show that the recyclable bio-epoxy exhibits comparable or superior properties when compared with conventional and non-recyclable bio-epoxies, particularly in terms of impact resistance. Recyclable epoxy, examined in the current study, shows a 73% higher strain energy release rate as compared to conventional epoxy. These results suggest that bio-epoxies could serve as a viable alternative to conventional epoxy.