## 7 Conclusions  
The evaluation of the results clearly shows that the application of the custom CNN developed in this study is capable of detecting the artificially generated defects with an accuracy of over 99%, and a further step has been taken towards possible process monitoring. It has once again demonstrated and confirmed that the application of deep learning is suitable for monitoring industrial processes and providing data that can then be used in further steps to actively intervene in the process. As it is otherwise hardly possible to intervene in the running process, the developed mechanical set-up shows a new possibility to simulate defects. Compared to the experimental sensor-based methods of process monitoring, this study proposes an effective method of defect detection and process monitoring that only requires sufficient training data with many different component geometries and scenarios of the process. This approach proved capable of providing real-time data analysis via the CNN and an online interface. This is followed by a discussion of the next steps in a complete process monitoring system to define the parameters responsible for the failure and derive appropriate countermeasures and continuous interaction with the in situ process control to modify the process inputs based on the measurements. However, this would require intervention in the machine parameters such as the active control of the temperature or exposure strategies, which was not part of this study.  
Once again, deep learning computer vision technology has demonstrated its ability to monitor industrial processes without human intervention. However, it should be emphasized that this approach has been adapted to the current PBF-LB/P system and that, from today's perspective, it is not possible to say how the CNN would react if a different additive manufacturing technology were used and whether the high error detection rate could then be maintained. It would be useful to further investigate the use of deep learning to monitor the ongoing process, including additional data such as physical influences or recordings of other AM processes, to compare defect detection results, or possibly to implement online parameter optimization.  
Conceptualization, V.K., T.A. and E.T.; Data curation, V.K., T.A. and E.T.; Formal analysis, V.K., T.A. and E.T.; Funding acquisition, V.K., M.B. and A.O.; Investigation, V.K., T.A. and E.T.; Methodology, V.K., T.A. and E.T.; Project administration, V.K.; Resources, V.K., T.A. and E.T.; Software, V.K., T.A. and E.T.; Supervision, V.K., M.B. and A.O.; Validation, V.K., T.A. and E.T.; Visualization, V.K.; Writing--original draft, V.K. and T.A., Writing--review and editing, V.K., T.A., M.B. and A.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research leading to these results was funded under the cooperative doctoral program \"Digightot\" between FH Campus Wien and TU Wien. This work was also supported by the City of Vienna: MA23--Projekt 29-22, \"Artificial Intelligence\", and MA23--Projekt 30-25, \"AI & VR Lab\".  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** A big thank you goes to [PERSON], who made this project possible with his expertise in handling and operating the laser sintering system.  
**Conflicts of Interest:** 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 the data; in the writing of the manuscript; or in the decision to publish the results.