## 6 Discussion
### Limitations  
There are several limitations, some of which have already been addressed in previous sections. The attempt was made to perform a 'fair' (equal) comparison [48] by presenting the same dataset in each comparison to both inspection agents. While the research sample was equal it was not necessarily equitable. The authors acknowledge that the research design was rather in favour of the technology and might have limited the human performance. This limitation was accepted to allow for the utilisation of eye tracking as part of this research project [9; 10]. In concrete terms, this means that in comparisons 1 and 2 the inspection agents were presented with images as opposed to borescope videos or physical parts. Only in comparison 3 were the operators given the blades, which allowed them to view them from different angles and use their tactile sense (further discussed below).  
The image processing software in comparison 1 was developed for detecting edge defects, such as nicks, dents, tears, tip curls and tip rub, and was unable to detect defects on the airfoil surface, e.g., airfoil dents. This decision was made due to edge defects being the most critical and common types of defects. To represent the real situation in MRO, a similar defect distribution was assessed, and thus most blades had damage on the leading and trailing edges. Only a small proportion of airfoil defects were included due to their are occurrence. Therefore, the performance of the software was only marginally affected by this limitation.  
The limitation in the borescope comparison was that both the human and AI were presented with images rather than borescope videos for the same reason as above. Seeing the blade rotating past the borescope camera from slightly different angles and under varying lighting conditions might provide additional information. With 30-60 frames per blade (depending on frame rate) the AI has a higher chance of detecting a defect than on a single image. Likewise, the operator has multiple frames per blade to detect any anomaly. However, the computational power of the software is much greater, and the AI can process every single frame, whereas the human operator might only perceive every fifth frame due to the rotational speed [107]. On the other hand, the operator has contextual knowledge and the cognitive ability to perceive motion, which might provide additional cues that hint at defects. In summary, both inspection agents were tested under somewhat non-ideal conditions; and performance might improve when presented with videos. This would be an interesting topic for future work.  
The 3D scanner created a model of the blade and compared it quantitatively with the nominal. The operator, in contrast, had to sense the blade using their eyes and fingertips to look for and feel for any irregularities in the shape. However, they were unable to quantify the deviation (in metric terms). Measuring tools (e.g., shadowgraphs) were not provided because in practice the operator would make a serviceability determination based on a (subjective) perception. Measuring every blade is time consuming and not economical.  
The weighted factor analysis in Section 5 attempted to provide a proof-of-concept for rating different inspection agents based on a variety of criteria. Both, the scores of the pairwise comparison and of the weighted factor analysis were assigned to the authors' best knowledge, but could be subjective, particularly for non-quantifiable factors. In future, the scoring process could apply the nominal group technique [108] to reduce the subjectivity of the scoring system.