## 6 Conclusions  
In this paper, a fully automated and generalized end-to-end workflow for visual recognition of AM parts that deploys on low-cost hardware was presented, optimized, and evaluated in a case study. First, the workflow sub-steps of image rendering, network training, and image classification were described in detail. The real-time image classification was realized with hardware components for approximately USD 200, and the image rendering and training to recognize a new batch of AM parts took four to five hours. Second, the workflow characteristics of the deployed neural network structure, the integration of a physics simulation and the total number of synthetic training images were analyzed using a custom test set to identify their influence on the AM part classification accuracy. According to the findings, the optimal workflow deploys VGG16 as the network structure, includes a physics simulation to increase the classification accuracy with fewer training images, and uses 256 synthetic images to be trained for 5 epochs. Third, the proposed workflow and its generalizability were evaluated in an industrial case study, where the workflow was used to recognize previously unknown AM parts from six different build jobs. On a variety of 215 distinct part geometries and 519 recognized parts in total, classification accuracies of 99.04% (top three) and 90.37% (top one) were achieved, supporting the industrial applicability of the workflow.  
The achieved results allow for the reproducibility of the workflow when implementing it for industrial applications of AM parts. While the top-one accuracy of 90.37% is not sufficient yet for full automation of AM post-processing, it is an important step toward this goal. Furthermore, the findings of this paper are relevant to similar industrial applications of visual object recognition where real training data does not exist or require high effort and expertise to be created. The only requirement is the access to the CAD data of the objects that are to be recognized, whereas the objects themselves do not have to exist yet. Furthermore, the documented findings allow for addressing specific challenges in the automated visual recognition of AM parts: mirrored and scaled parts.  
These challenges will be addressed in future research by adapting training parameters and including further information in the classification process. Additionally, it is planned to increase the amount of evaluation data and extend the proposed workflow to additional AM processes than MJF. Finally, methods to recognize multiple parts in a single classification step will be explored, as this could further reduce recognition times.  
Conceptualization, J.C. and D.O.; methodology, J.C.; software, S.R.; validation, S.R., J.C. and D.O.; formal analysis, J.F.; investigation, J.C.; resources, M.M.; data curation, S.R.; writing--original draft preparation, J.C.; writing--review and editing, J.F.; visualization, J.C.; supervision, M.M.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Swiss Innovation Agency, Innosuisse, under grant 50383.1 IP-ENG.  
Not applicable.  
The test dataset is available under the following link: [[https://data.mendeley.com/datasets/trd8](https://data.mendeley.com/datasets/trd8) nry345/1]([https://data.mendeley.com/datasets/trd8](https://data.mendeley.com/datasets/trd8) nry345/1) (accessed on 23 October 2023).  
The authors would like to thank [PERSON] and [PERSON] for their support of the project.  
The authors [PERSON], [PERSON], and [PERSON] were employed by the company inspire AG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.