Machining of composite materials is a challenging task due to the heterogeneity and anisotropy of composite structures. The induced defects reduce integrity of the machined surface as well as the loading capacity of the composite structure in service. Therefore, it is necessary to quantify the damage induced during trimming and correlate the quality of the machined surface to mechanical properties. The correlation of the surface roughness criteria, widely used in literature, to the mechanical behavior raise several contradictions. For this reason, new parameters for the characterization of the machined surface are proposed and correlated to the mechanical behavior under compressive loading. In this context, carbon fiber-reinforced plastic laminates are conventionally trimmed, and the machining damage is characterized using scanning electron microscope observations, X-ray tomography, and 3D optical topography. The results reveal that crater volume and maximum depth of damage quantify the machining damage more realistic compared to the classical surface roughness criteria.

The purpose of this study is to evaluate and compare the ability of various composite structures to dissipate the energy generated during a crash. To this end, circular composite tubes were tested in compression in order to identify their behavior and determine their absorbing capabilities using the specific energy absorption (energy absorbed per unit weight). Several composite tubular structures with different materials and architectures were tested, including hybrid composition of carbon–aramid and hybrid configuration of 0/90 UD with woven or braided fabric. Several inventive and experimental trigger systems have been tested to try and enhance the absorption capabilities of the tested structures. Specific energy absorption values up to 140 kJ.kg⁻¹ were obtained, achieving better than most instances from the literature, reaching around 80 kJ.kg⁻¹. Specimens with 0°-oriented fibers coincidental with the direction of compression reached the highest specific energy absorption values while those with no fiber oriented in this direction performed poorly. Moreover, it has consequently been established that in quasi-static loading, a unidirectional laminate oriented at 0° and stabilized by woven plies strongly meets the expectations in terms of energy dissipation. Incidentally, an inner constrained containment is more effective in most cases, reducing the initial peak load without drastically reducing the specific energy absorption value.

This paper presents a Non-Destructive Testing (NDT) method based on the penetration properties of terahertz (THz) waves. A CW raster-scanning THz imaging setup, using a 3.8 THz Quantum Cascade Laser as a source, is used to perform NDT of polypropylene/polypropylene composite samples.Results from transmission and reflection THz imaging are compared to ultrasound C-scan. THz images in reflection give similar results to C-scan whereas THz transmission images provide more information about delaminations and cracks in the fiber fabrics

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.