## 1 Introduction
The traditional form of the DED process is used with a wire feedstock drawing inspiration from the traditional welding process. The other technique that utilizes powder flow from the feedstock is called Laser Engineered Net Shaping (LENS) [20]. In our study, we utilize Laser-based Directed Energy Deposition (LB-DED) with a powder flow method for the production of metallic workpieces. Performance of parts is highly contingent upon which AM process is selected for production. The AM process is critical for desired mechanical properties in layer-produced parts, as seen in the study by [21], tensile and impact strengths are strongly dependent on the layer building direction. Recent studies are focused more on nondestructive internal dynamic defect monitoring (or quality monitoring) of metallic parts [19; 22]. One major issue with part defects is the presence of undesired cracks and low-density occurrences such as porosity. Researchers demonstrate a heavy interest in methods to identify and classify these malfunctions to avoid part defects [23; 24; 25; 26; 27]. Acceptable low-density occurrences such as porosity numbers and sizes vary based on the application. Most applications aim to limit the size and number of low-density occurrences in parts to produce fully dense specimens. Other literature shows that low to no density occurrences and cracks that are accumulated throughout the build process negatively hinders parts and causes defects [28; 29]. With this theme in mind, our focus was to create a process in which we can monitor and possibly control low-density occurrences such as porosity sizes and numbers. In-situ monitoring for workpiece quality is a popular topic in the AM community. There have been studies that aim to measure printer parameters in real-time to quantify changes in the printing conditions [30]. Neural Networks (NN) are being used for a variety of in situ applications. For example, data-driven Artificial Neural Networks (ANN) have been used in an attempt to predict characteristics of porosities in a part based on features during the build [31]. The study conducted in [31] demonstrates a framework of how NN's can be used for the evaluation of low-density occurrences from real-time datasets. Faulty parameters lead to the possibility of pronounced porosities, cracks/corrosion, and internal residual stresses in the workpiece [32; 33]. Research demonstrates a great desire to monitor porosity in real-time because a specimen with pronounced low densities (porosity) will often exhibit poor mechanical performance [34; 35]. As a result, research has trended towards using acoustic emission for identifying cracks/corrosion in real-time [33; 36; 37]. Since acoustic emission data is rich, these investigations use Deep Learning (DL) techniques for prediction. There is a scarcity of literature focused on classifying layer quality with a real-time evaluation model based on layer features. In addition, we believe understanding the issue as a time-series component could lead to an acceptable method of determining how or if a poorly produced layer in the incipient stage of a print has drastic effects later on in a print causing multiple poor layers resulting in workpiece defects. Where [21] focused on mechanical properties to provide insight into manufacturing principles of the AM process, this study is focused on creating a benchmark and providing insight into the features that most affect internal quality at the layer level of the LB-DED process. Based on an adjustable threshold, we have developed a method that classifies layers as acceptable or unacceptable based on low-density occurrences such as porosity size and quantity. We seek to examine and illuminate this complex problem and provide insight into classifying layers based on only print features during the build process. The novelty of this investigation is two-fold. First, we utilize a synchronously collected in situ dataset with a low sampling rate in conjunction with an ex-situ evaluation based on CT scans of parts to create a labeling dataset.