## 3 Results and Discussion
### Analysis of the Initial Specim State  
Figure 14 shows the resulting images of a test specimen section with inserted voids in the initial state from the high-resolution XRM measurements. Styrofoam spheres were placed in the area of the filler where their imprints left circular structures of different diameters in the sectional images. The spherical structures only become visible in the 3D images. The voids can be analyzed according to position, size, sphericity, and size distribution in VolumeGraphics, or their volume can be visualized in color-coded form, see Figure 14b. However, the analysis is time-consuming, so deep learning methods were further used for data analysis.  
\begin{table}
\begin{tabular}{c c} \hline
**Experimental Parameters** & \\ \hline Excitation power & 2 kW \\ Range of wavelet & 7.5–14 μm \\ Thermal resolution & 0.035 K \\ Image size & 640 px \(\times\) 480 px \\ Recording time & 30 s \\ Recording frequency & 10 Hz \\ \hline \end{tabular}
\end{table}
Table 4: Overview of the experimental parameters used.  
\begin{table}
\begin{tabular}{c c} \hline
**Specimen with Defect Type** & **Excitation Time \(t_{exc}\) [s]** \\ \hline Reference without defects & 20 \\ Voids in the coating/filler & 10 \\ Voids in CFRP & 30 \\ Delaminations & 30 \\ \hline \end{tabular}
\end{table}
Table 3: Overview of the different excitation times of the respective test specimens.  
Figure 15 shows the results of the employed deep learning methods. In Figure 15, one slice of the CT images is shown. The different layers of fibers and the filler are clearly visible. The void in the coating, the coating itself, and the resin of the GFRP laminate are barely visible due to their almost similar density. Therefore, the XMR data segmentation was done using U-Net (see Figure 15. The U-Net was trained 12 times and an additional virtually labeled frame from different parts of the specimen was fed into the training data during each training. The average training duration was around 35 min. Since the results of the segmentation were inaccurate in the coating layer and did not ameliorate with further training, a Sensor 3D [25] CNN with a different architecture was examined for better results. The results using the Sensor 3D shown in Figure 15 were obtained after five training sequences, i.e., with five virtually labeled frames. Nevertheless, the training of the Sensor 3D is more time-consuming than that of the U-Net, with an average training duration of 68 min. The comparison of the achieved segmentation results using the U-Net and the Sensor 3D models depicts a clear advantage of the sensor 3D over the U-Net for the current purpose. The Sensor 3D CNN could not only differentiate between different material layers and the background, but the defects were also completely and correctly located.  
Figure 14: Image analysis on high-resolution XRM data of a specimen with voids in the filler. In (a) sectional images and 3D view in VoliumGraphics without analysis. In the area of the trovel application, there are particles with high density (bright dots) as well as artificially generated voids (dark circles) in the test specimen in (b) void analysis with color-coded volume sizes, adapted with permission from [19].  
Figure 15: (a) CT slice (no. 303 out of 2014 images), (b) segmentation using U-Net, (c) segmentation using Sensor 3D [19].  
In conclusion, manufacturing reproducible test specimens close to the structure and geometry of a real rotor blade leading edge and the inserted styrofoam balls in different coating system layers (filler and coating) to imitate voids was successful.