By using fiber-reinforced plastics as the material for lightweight structures, adhesive bonding becomes an increasingly focused solution for joining these components. In comparison to bolted joints, adhesive bonding has a lot of known benefits, e.g. sealing, no weakening of adherends and a homogeneous load transfer. However, the stress distributions in single-lap joints are not homogeneous all over. Both the shear and peel stress distribution have a peak at the overlap ends of the joint and a lower loaded part in the middle, as shown by Volkersen and Goland and Reissner. In order to reduce these stress concentrations, there are known concepts for the modification of the joint. In this paper, adherend chamfering, different adhesive spew fillet geometries, and the mixed adhesive joint are investigated and compared to a novel local adherend surface toughening concept by using a thermoplastic PVDF layer. A high-resolution digital image correlation is used to visualize the deformation in the bondline. While the state-of-the-art used concepts do not show a high increase of joint strength for single lap joints, the joint strength for the surface toughening specimens can be increased by up to 84%.

Composite materials, such as Carbon Fiber Reinforced Plastics (CFRP), are widely used across industries due to their exceptional mechanical properties and lightweight nature. With increasing interest in hydrogen storage applications, a comprehensive understanding of CFRP’s microstructure is crucial. However, there remains a critical gap in effectively linking microstructural characteristics to multiscale simulation models for improved tank design. This study presents a detailed analysis of CFRP microstructure using micro-section image analysis. Advanced artificial intelligence techniques, including transfer learning with VGG16 and XGBoost, are employed to detect fibers and matrix-rich regions. Notably, the model achieves an impressive Area Under the Receiver Operating Characteristic (AUC) of 0.89, providing strong quantitative validation. Furthermore, the experimentally determined fiber volume fraction of 0.59 closely aligns with the predicted value of 0.589, demonstrating the model’s accuracy. The investigation extends to analyzing fiber distribution using statistical features such as Voronoi polygons and kernel density estimation. Through dimensionality reduction via t-SNE and clustering, distinct patterns in CFRP’s microstructure are identified, offering deeper insights into its complexity. Finally, an explainable artificial intelligence (XAI) framework is implemented using a surrogate model to map the decision-making process to microstructure characteristics. The surrogate model achieves an F1-score of 0.90, reinforcing the reliability of the approach. This research provides valuable insights into the distinct categories in CFRP’s microstructure, particularly relevant for utilization in multiscale numerical methods.