## 5 Conclusions  
Testing in the simulated stages gives us a robust assessment of the feasibility and affordability of the model trained for image processing. By providing us with the probability of confidence of the developed algorithm, we can safely infer that the model is suitable for use, since, when calculating the average of the data in stage \"A\", an average confidence of 0.97 is obtained in the effectiveness of the element detection system. This high degree of confidence in the detection of elements in the manufacturing cell ensures that the results  
Figure 7: A real printed circuit board manufacturing cell.  
are highly reliable and accurate in the effective operation of the computer vision system relative to the elements of the manufacturing cell.  
In stage B, as inferred from Table 3, calculating the average confidence of the detected elements shows a mean of 0.97, similar to stage A. In stage C, the average confidence of the detected elements was 0.98. This indicates that the results in Table 4 achieved a slightly higher confidence percentage than the two previous stages. Overall, combining the confidence percentages from stages A, B, and C, the model achieved an average confidence of approximately 0.973, reinforcing the reliability and accuracy of the element detection system across different stages.  
The ultimate goal of these tests in a simulated environment is to optimize the actual manufacturing cell, reduce cycle times, minimize waste, and ensure product quality. This not only increases productivity but can also have a positive impact on production costs and time savings in manufacturing each printed circuit board. Having previously performed the tests in the simulation environment, we are better prepared to effectively manage and optimize the process in the future.  
In manufacturing cells or industrial environments where there is interaction between people and industrial elements, variations can occur, such as changes in element positions, the absence of certain components, or the presence of foreign objects. A system that constantly monitors these variations and provides feedback to robotic arms to update or avoid certain movements or actions can improve process times and detect potential errors in advance. By first performing these actions in simulation to validate their safety, we can ensure that the robotic arms behave appropriately in real life. Future research should incorporate this type of detection in a real environment to validate changes in action execution based on the detected element variations.  
Conceptualization, A.T.-M. and L.B.-R.; methodology, A.T.-M. and M.B.-O.; software, A.T.-M. and M.B.-O.; validation, A.T.-M., M.B.-O. and L.B.-R.; formal analysis, E.A.F.-U. and C.E.C.-G.; investigation, A.T.-M. and M.B.-O.; resources, E.A.F.-U. and C.E.C.-G.; data curation, M.B.-O.; writing--original draft preparation, A.T.-M. and M.B.-O.; writing--review and editing, E.A.F.-U. and A.T.-M.; visualization, A.T.-M.; supervision, E.A.F.-U.; project administration, A.T.-M and E.A.F.-U.; funding acquisition, E.A.F.-U. and C.E.C.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available at: [[https://github.com/trejoan1/elementdetection.git](https://github.com/trejoan1/elementdetection.git)]([https://github.com/trejoan1/elementdetection.git](https://github.com/trejoan1/elementdetection.git)) (accessed on 10 June 2024). The source code was typed in Python 3 using Visual Studio Code.  
The authors are grateful for the support received from the CIDESI Research Center, Mexico, and also to the Mexican Science Council CONAHCYT, as the work described in this document is the result of the research project number QID029/QID036.  
The authors declare no conflicts of interest.