## 4 Conclusions  
In this study, we present a nondestructive synchronous method that monitors quality of a workpiece during a metal AM build process. The investigation innovates based on two main concepts. The first concept is that we use a synchronously collected in situ dataset with low sampling rate in conjunction with an ex-situ evaluation based on CT scans of parts to create a labeling dataset. The second concept is the application of a CNN algorithm that classifies inter-layer quality through the build process of a part based on in situ input data and ex-situ labels. This method resulted in a dataset of process parameters collected during in situ quality monitoring. The printed workpiece was analyzed for low-density occurrences via a CT scan. An adjustable threshold was created to qualify specific layers as acceptable or unacceptable. Using a combination of the synchronous process parameter dataset and layer qualification dataset, we investigate the importance of specific features during real-time LB-DED printing at the inter-layer level. We show that LP, MPS, and SLED have the most impact on prediction, with CF having a strong correlation to those three features. We explore the relationships between features to establish that certain features are correlated with each other while the workpiece is being produced. In this investigation, we demonstrate that even though the use of a specific material and geometry does limit the generalizability of the results obtained, possibly leading to over-fitting, a DL classification model is capable of classifying acceptable and unacceptable layers and could be implemented into an in situ quality monitoring setting through training of features during a build process at the interlayer level. We use the layer qualification dataset to establish a set of labels that are used for training with the synchronous process parameter dataset as inputs. Compared to similar in situ quality monitoring AM studies, we do not utilize acoustic emission or other NDT data with high sampling rates (e.g., MHz) for classification and show the possibility of using a dataset with a low sampling rate (e.g., Hz) for simple and reduced data size training in the classification models. Results indicate over-fitting occurs when training and testing on a sample of a similar shape. Further analysis confirms over-fitting from the CNN when training and testing on separate samples, however, the CNN model is promising for in situ layer classification. The AM process has limited features which could be one reason why the classification model is overfitting. The addition of other NDT methods may remove statistical bias and may improve the information mapping process of the algorithm. For example, adding acoustic emission data in parallel to recording the printing parameters would allow for deeper and more accurate monitoring as the AM machine builds parts. We are confident that adding such a dataset to the classification model would increase the robustness of the training and testing process and reduce statistical bias of the algorithm. Conceptualization, E.D.-N., K.L. and H.S.H. ; methodology, M.J., K.L. and H.S.H. ; software, S.H. ; formal analysis, S.H. ; investigation, S.H., M.J., H.S.H. and E.D.-N.; resources, E.D.-N., M.J., K.L. and H.S.H. ; writing--original draft preparation, S.H. ; writing--review and editing, S.H., M.J., and E.D.-N.; visualization, S.H., E.D.-N.; supervision, E.D.-N.; project administration, E.D.-N.; funding acquisition, E.D.-N. All authors have read and agreed to the published version of the manuscript. Not applicable. The datasets generated during and/or analysed during the current study are not publicly available due to a nondisclosure agreement as part of collaborative NASA STTR Phase I project between FormAlloy Company and New Mexico State University but are available from the corresponding author on reasonable request. **Acknowledgments:** The authors would like to express their sincere gratitude to FormAlloy Company in particular [PERSON] and [PERSON] for their support.