## 1 Introduction  
The progressive development of even larger wind turbines due to the continuously growing demand for wind power, an important renewable energy technology, results in increased tip speeds at the leading edges of modern rotor blades. With this development, the damage potential and impact of rain erosion have also increased [1]. Rain erosion damage to the rotor blade surface reduces performance and affects the lifetime of wind turbines [2]. Accordingly, rain erosion is an important aspect to be considered in wind turbine operation. The current assumptions about failure mechanisms due to manufacturing defects in material, coatings [3], and the interface [4; 5] need to be reduced to increase the understanding of rain erosion. Rain erosion damage on the leading edge of a wind turbine rotor blade has already been investigated. For example, a guideline to qualify glass fiber-reinforced plastic (GFRP) coating systems for rotor blade applications was developed [3]. It is based on test specimens that were manufactured and eroded in the erosion test rig, and the damage was subsequently evaluated using destructive measuring methods, e.g., micrograph analyses. The test specimen condition before rain loading was not considered but can be essential for the occurrence of erosion damage in many cases. Although this guideline-compliant approach succeeds in distinguishing between different coating systems in terms of their performance, no validated theories to date explain the formation mechanisms of erosion damage. Moreover, it does not allow any statement to be made regarding the expected time course of the damage progress. However, this is important for enabling the early detection of the damage and assessing the damage with respect to the decision and planning of a repair. In recent years, X-ray computed tomography (CT) has found wide application in materials science research [6; 7; 8]. Furthermore, the determination of fiber orientation in short fiber injection molded components [9] using CT is of great importance, as well as the use and analysis of in-situ experiments for a deeper understanding of the failure mechanisms of fiber-reinforced wind turbine blades. A cyclic study performed by [PERSON] [10] investigated the degradation of plate-like fiberglass composite structures in an erosion test rig. An evaluation of the degradation was carried out using CT examinations and analyses. The removal of the coating between the individual cyclic loads was determined using a voxel size of 14.8 um\({}^{3}\) (Nikon XT H 225 LC X-ray system). [PERSON] [11] studied a 2 cm\({}^{3}\) section of a wing leading edge stressed in a rain erosion test rig that was inspected by scanning electron microscopy and X-ray microscopy (XRM, Zeiss Xradia 520 Versa). The specimen examined included intact as well as damaged areas. With a voxel size of 2 um\({}^{3}\), the defects were detected and documented by XRM. The data formed the basis of computer-aided micromechanical modeling of the effect that structure and coating properties have on rotor blade leading edge degradation. A causal analysis of the degradation was based on this simulation. However, all of these select systematic studies do not contain any early detection or time tracking of damage and failure due to rain erosion, so the failure mechanisms are not yet fully understood [4]. In conclusion, intimate material damage has not been considered in a specific and repeatable way, so the relationship between the initial condition of the specimens prior to erosion exposure and the damage patterns that occur after erosion exposure has not yet been investigated. Lately, the use of artificial intelligence (AI) for the automated evaluation of large data sets is becoming crucial. In the field of engineering, the methods of deep learning could be applied to defect detection and differentiation in GFR components. This requires datasets of CT slices, which are divided into training, testing, and validation datasets.