## 3 Results and Discussion
### Analysis of the Erosion Evolution Using CT and XRM  
In Figure 16, the 3D representation of the loaded specimen with three areas of different degrees of degradation in the filler layer is marked and shown in the left partial view (red circles). Corresponding sectional images showing the area before and after rain loading are shown on the right. Voids are present in the filler layer. It can be seen that the degree of degradation depends on the position of the pore, i.e., the distance to the air-coating layer interface, the size and number of voids, and the void volume. From the sectional images, a \"time history\" of the damage dynamics can be derived. At first, the coating layer gets broken and is therefore no longer present as protection of the filler, and cracks appear in the filler layer (Figure 16a). Second, the filler layer gets removed by abrasion or washed out (Figure 16c) until the GFRP structure is exposed (Figure 16b). Afterward, the bursting raindrops wash out the boundary layer between the GFRP and the filler, and cracks form outside the opening. The damage there is more extensive than the opening in the coating layer. Figure 16: 3D representation of the loaded specimen (**left**) with marked positions (red circles) of the sectional view. The sectional views (**right**) show identical positions (**a–c**) before and after loading in the rain erosion test rig, adapted with permission from [19]. The red arrows mark voids in the GFRP, surface damage, or cracks in the material. Figure 17 shows a cross-sectional view of the identical defect from Figure 16b at three different locations. Delamination can be seen along the interface between the GFRP and the filler (Figure 17a). The filler has been destroyed over a large area, and the extent is significantly greater than the opening in the coating layer (Figure 17b). A visual inspection of the defect from the outside would not capture the real extent of the destruction. For a faster evaluation of the test specimen's state, the version of the Sensor 3D training with the segmentation wizard toolbox was subsequently used to automatically label the 2014 slices of the CT scan. The 3D results of the initial test specimen state are shown in Figure 18a. The black 3D geometrical half cylinder represents the scanned specimen, and the spheres colored purple represent the defects. The Sensor 3D labeled the artifacts, the noise, and both ends of the specimen along the longitudinal edges as pores. This indicates that further training of the CNN was required to reduce noise. After loading the specimen with rain in the rain erosion test facility, the specimen was scanned again, and the same procedure was applied to the new data set. The results are displayed in Figure 18b. The effect of noise and ring artifacts is visible in this scan. Nevertheless, the defects were successfully located. The voids increased in size due to rain erosion. The trained CNN showed that the accuracy and training based on a handful of frames were sufficient for the segmentation of the whole data set. Another training strategy needs to be developed to ensure the robustness of the CNN. The training of neural  
Figure 17: Cross-section of the identical defect (XRM, 9 μm pixel size) at three different locations (**a**) at the location with the deepest defect, (**b**) at the boundary location of the defect, and (**c**) at various locations of the defect [19]. Figure 18: Defect detection (**a**) in the initial state and (**b**) after rain loading, adapted with permission from [19]. networks required a small amount of annotated data sets, so the biggest hurdle to using AI was overcome. With the help of the trained networks, the data analysis can be accelerated with modern hardware, especially with a powerful graphics card. A relevant aspect at this point is the use of cloud computing as an alternative to modernizing the existing hardware.