## 2 Powder Bed Fusion
### State of the Art Studies  
Detecting porosities in PBF processes has a significant contribution to improving part quality [17]. The porosity during the laser additive manufacturing process was monitored  
Figure 8: 2D images of different keyhole pores morphologies [6]. Figure 9: Lack of fusion pores morphology [16]. using a deep learning-based method, namely convolutional neural network, that predicts the porosity based on melt pool cross-section images acquired by coaxial camera [18]. The model achieved an accuracy of 91.2% in detecting porosity occurrence, and all misclassified samples have pores less than 50 \(\upmu\)m. This limitation in detecting micropores is one of the issues in applying convolutional neural network for pores prediction. Similarly, a lack of fusion porosity was predicted based on melt pool data collected during the selective laser melting process, and the DMP monitoring system was used to monitor the melt pool during the processing with titanium alloy material. Next, they created an algorithm that correlated the porosity with melt pool monitoring data, which achieved 90% prediction sensitivity for events with a lack of fusion pores greater than 160 \(\upmu\)m volume [19]. Recently, acoustic signals have been used in porosity classification, particularly in achieving the balance between classification accuracy and spatial resolution in porosity detection. High sensitivity fiber Bragg grating sensor was adopted to collect acoustic signals for airborne. The time span for each running window was 160 ms, and 300 patterns represented each of the three porosity levels without overlapping between training and testing set in terms of the running window. The parts with different porosity levels were classified using spectral CNN with a classification accuracy between 83-89% [20]. Using CT scans of sample parts, there is a problem related to assessing accurately the porosity in SLM parts [21]. One main drawback is clear when using gray value analysis to assess the porosity of SLM parts visible in CT scan slices. The difficulty is the subjectivity in selecting an appropriate grayscale threshold that would convert a single slice into binary images highlighting defective regions, as well as determining the true level of porosity. For example, in Figure 10 when an inappropriately low grayscale threshold is used for binary image conversion for the original slice, an amount of tiny undesired white spots is not filtered, as shown in Figure 10b. However, adopting a high threshold causes significant alterations in the morphological features of the defective area, more specifically near the boundary, as shown in Figure 10c. These thresholds would result in significantly different predictions of porosity levels. To evaluate this issue further, the authors of this study [21] proposed an empirical method to estimate the porosity in finished SLM parts using a naturalized threshold for CT scan slices. When compared with the Archimedes method, they found similar increasing or decreasing trends in the predicted percentage of porosity, and concluded that the naturalized greyscale threshold is not the best method for porosity estimation in CT scan slices due to radiodensity variation and CT setup mutual influence. Thus, compensating for these variations is difficult with a unique grayscale [21]. This limitation is found in one of the main existing methods used in porosity estimation, along with the Archimedes method, as mentioned in [22]. Thus, this paper addresses this gap by proposing a new approach based on the use of a Regression Convolutional Neural Network (RCNN) algorithm to predict the percent of porosity in CT scans of finished SLM parts, without the need for subjective difficult thresholding determination to convert single slice to a binary image. In  
Figure 10: Original slice and binary images [21].