## 6 Discussion
### Future Work  
Several future work streams have been proposed previously and are not repeated here. It is well understood that manual inspection is not error free, while full automation of the inspection processes is not always technically feasible or economical [77]. Thus, hybrid systems, combining the strengths of both human operators and advanced technologies, offer potential for future research. Rather than comparing the individual performances and capabilities of each inspection agent independently, the combined performance of such technology integrated inspection systems could be assessed, i.e., do human operators perform better when aided by technology? Attribute agreement analysis and eye tracking could be used to extract any improvements in their performance and search approach. There are several conceivable scenarios concerning the integration of human operator and technology: (a) the technology could be used as a 'pre-scan' highlighting any anomalies and leaving the defect confirmation and serviceability decision to the operator, (b) both agents inspect the parts independently and only in cases of disagreement is the expert is consulted. Those and other options could be evaluated to ensure optimal utilisation of both agents.  
There is a potential that by implementing a hybrid inspection system, the operator undergoes some form of training, as they must evaluate not only their own findings but also the ones of the aide. This might improve the underlying mental model [9,10] of the operator to the extent that their sensing gets stimulated by the findings of the aide, i.e., other conditions and defect manifestations that would have otherwise not attracted the operators' attention. Future work could investigate the possibility of using advanced technology for training purposes.  
Previous work [10,87] has indicated that human operators have been inaccurate in their defect classification. The classification performance was not tested in the present study due to the limitations of the technological systems. In principle, however, it is possible to program a classification algorithm and compare it to the accuracy of the human operator. The classification accuracy could then be added to the weighted factor analysis.  
SHERPA (developed by [109]) applies hierarchical task analysis to anticipate deeper causes of human error. The present study did not explore for such deeper causality, and to do so would require the prior development of a classification system or taxonomy of human errors. This might be an interesting and useful avenue of future research. The SHERPA taxonomy (action, check, retrieval, communication, selection) may or may not be the best way forward for the specific case of visual inspection, as it is weaker on the cognitive processes than the existing visual inspection framework [10]. Somewhat related work, from a SHERPA perspective, is evident in [110] for the construction industry. Possibly a Bowtie approach might be considered because this is well attested in the aerospace industry. Furthermore, a taxonomy of blade defects already exists [111], as does a Bowtie of human error for the inspection task [13; 14].