## 5 Conclusions and Future Research
Following the validation of our hypothesis that a more constrained _ad hoc_ solution specific to the final deployment scenario is preferable to a generalist approach, it would be interesting to further improve the system by employing prior knowledge ensuring that vehicle parts that do not physically fit a certain chassis would not be detected. This is only possible because, in the proposed Industry 4.0 scenario with a digital conformity list, the system knows, at any given moment, which vehicle version is being assembled and, thus, which parts would (not) fit its chassis. Depending on the results obtained on the factory floor, we plan on replacing the kSim9 vehicle generator with official Computer Aided Design (CAD) part models, for higher fidelity and better automated detection results. We also intend to expand the part library to include a wider variety of vehicles beyond the k9 van model and adapt the visualization module into a wider variety of mobile devices, that best suit different workers' needs. Other next steps include closing the loop by integrating the workers' validation of the detected deformities and non-conformities to correct the automated detection module in an active learning setup. We also plan on further integrating the worker's expertise by adding to the visualization module the pseudo labeled real images for each button for individual evaluation, rather than only presenting the simulated version of the parts. This way the worker can give the object detector more fine-grained feedback regarding the detection itself and not only about the classification. Thus, this would constitute a truly hybrid assembly line, where the machine helps the worker and vice-versa, and where the overall accuracy and efficiency of the quality control process is improved because it leverages the best of both worlds in true symbiosis. Conceptualization, A.T.C., I.R.-T., V.F. and L.F.T. ; methodology, A.T.C., I.R.-T., P.P., V.F. and L.F.T. ; software, A.T.C., I.R.-T. and P.P. ; validation, A.T.C., I.R.-T., P.P., V.F. and L.F.T. ; formal analysis, A.T.C. and I.R.-T.; investigation, A.T.C., I.R.-T. and P.P. ; resources, V.F. and L.F.T. ; data curation, A.T.C. and I.R.-T.; writing--original draft preparation, A.T.C. and I.R.-T; writing--review and editing, V.F. and L.F.T. ; visualization, A.T.C. ; supervision, V.F. and L.F.T. ; project administration, V.F. and L.F.T. ; funding acquisition, V.F. and L.F.T. All authors have read and agreed to the published version of the manuscript. This work was funded by Project \"INDTECH 4.0--New technologies for smart manufacturing\", No. POCI-01-0247-FEDER-026653, financed by the European Regional Development Fund (ERDF), through the COMPETE 2020--Competitiveness and Internationalization Operational Program (POCI). The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Universidade de Tras-os-Montes e Alto Douro (Ref. Doc16-CE-UTAD-2022, 30 March 2022). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Restrictions apply to the availability of these data. Data were obtained from PSA Mangualde and are available from the authors with the permission of PSA Mangualde. We would like to acknowledge the preliminary work carried out by [PERSON], without which this project would not have been able to take off. We would also like to thank [PERSON] for her very useful insights in graphic design and information visualization. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.