## Materials and Methods
### Analysing Methods  
#### 2.3.1 XMR and CT with Artificial Intelligence Evaluation  
For the investigation of the inner structure of the specimens, CT and XRM are commonly used non-destructive measuring methods; see Figure 6. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Settings** & \\ \hline rotation speed & 120 m/s \\ drop size & 1–2 mm \\ rain intensity & 20 L/min \\ ambient temperature & 23 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the parameter settings of the rotating rain erosion test facility. Figure 5: (**a**) Clamped specimens before rain erosion testing, (**b**) clamped specimens in between rain erosion testing, and (**c**) specimens after rain erosion testing, adapted with permission from [19]. Figure 6: Schematic structure of the X-ray techniques: CT: geometric magnification, the object must be placed close to the source and reduced in size for a maximum magnification, XRM: geometric and optical magnification, using a cotton ball and seeds as examples, a reduction of the object is not necessary. In contrast to the conventional \(\mu\)-CT, the XRM uses a two-stage magnification technique based on synchrotron caliber optics [8,9]. The geometrically enlarged projection is further magnified using a scintillator layer that converts X-rays into visible light, which can then be optically magnified using different objectives mounted to a microscope turret. The two-stage setup provides _Resolution at a Distance_, allowing large, flexible working distances while maintaining submicron resolution (spatial resolution \(<700\) nm) without the need to downsize the specimen. Figure 7a shows an example for an initial overview XRM scan capturing an \((28\times 30\times 2)\) mm\({}^{3}\) glass fiber-reinforced object. A cross-sectional image by using a 0.4\(\times\) magnification optic where the pixel size is 32 \(\upmu\)m is shown in Figure 7b. Once a region of interest (ROI) is determined, the distances between the source, object, and detector along the optical axis are adjusted according to the desired magnification. In addition, the component perpendicular to the optical axis (source-detector) can be adjusted so that the ROI remains on the optical axis during component rotation. The second scan captures a volume of \(2.8\times 3\times 1.8\) mm\({}^{3}\) when using the lens with a 4\(\times\) magnification (Figure 7b). Here, the pixel size decreases to 2 \(\upmu\)m. In the digital enlargement of the scan, the four glass fiber layers, the matrix, the filler layer, and a pore can be seen. The data generated by \(\upmu\)-CT is of large scale. With around 2000 cross-sectional images per scan, split images obtained from CT measurements require an efficient data processing scheme because the manual search for defects in a scan made of several slices is time-consuming. Therefore, machine and deep learning were applied as a means to locate defects in the specimens. Deep learning segmentation requires graphical computing power, which in this project was provided by an NVIDIA Quadro M5000 with 8 GB of GPU memory. It was capable of supporting CNN models up to 8,593,350 parameters. The defects in this work are mainly styrofoam spheres, modeling voids in the coating and/or filler layer, and glass fiber-reinforced laminate. The primary challenge limiting the success of AI for engineering applications is the availability of labeled data sets. Such labeled data is needed to train the neural network. For this reason, the first step towards automated data processing using AI is to generate meaningful labeled data. The labeling was done using the segmentation wizard [21], an image processing software. ORS Dragonfly [22] was used to generate the virtually labeled data for training, and the built-in untrained neural networks were used. A virtual rectangular white frame shown in Figure 8 represents a part of the specimen within a black background.