## 2 Background and Methodology  
Recent studies and literature reviews show that the use of deep learning is becoming increasingly common, especially in the manufacturing industry, to monitor processes, detect defects, and predict component quality [46; 47; 48; 49]. According to the literature, the application of supervised CNN in particular has often been used to check relevant components. The use of supervised CNN has shown promising results for tasks such as defect classification and defect segmentation. Classification works at the image level and recognizes the object in the image. Segmentation operates at the pixel level and detects the object type in each pixel of the input [46]. The following are a few examples of the use of supervised CNN in industrial and safety-related applications. In [50], [PERSON] and [PERSON] proposed a CNN to detect cracks in safety-relevant parts of a nuclear power plant. [PERSON] et al. [51] addressed the identification of small abnormalities in applications such as industrial inspection. A steel plate defect inspection was proposed by [PERSON] et al. [52] for real-time quality control of the manufacturing process. In [53], [PERSON] et al. showed the effective deployment of CNN  
Figure 2: Part shifting leads to total process abortion at the PBF-LB/P system used in this study. Components were completely torn out of the powder bed via the coater blade. The inside of the machine must be completely cooled and cleaned before the entire job can be restarted. for automated underwater pipeline defect inspection. [PERSON] et al. [54] introduced a segmentation-based deep learning approach for surface defect detection, which was designed to detect and segment surface anomalies and showed that the proposed approach is able to learn on a small number of defects, using only about 25-30 defective training samples instead of hundreds or thousands. Another industrial non-destructive testing (NDT) example is given by [PERSON] et al. in [55], who inspected X-ray images of castings using spatial attention bilinear CNN. In [56], an online sequential classifier and transfer learning (OSC-TL) method was presented by [PERSON] et al. for the training and classification of Mura defects in the manufacturing process of flat panel displays. In the literature, unsupervised CNN models are mainly categorized into anomaly detection, GAN-based models (Generative Adversarial Networks), and hybrid models. Compared to supervised CNN architectures, unsupervised models are able to perform image labeling, pixel-level, defect image classification, and defect area localization. External and internal defect detection is possible within several computer vision tasks [46]. The following are a few examples of unsupervised CNN applications for anomaly detection, GAN-based, and hybrid models. In [57], [PERSON] et al. proposed a convolutional autoencoder (CAE) to implement the anomaly detection of defects on concrete structures and further facilitate the visual inspection of civil infrastructure. Another example of an autoencoder is given by [PERSON] et al. in [58]. A multi-scale, fully convolutional autoencoder (FCAE) was used, considering only defect-free images for surface defect detection. [PERSON] et al. [59] used a deep support vector description (D-SVDD) CNN architecture for semi-supervised and unsupervised anomaly detection. Regarding unsupervised GAN models, [PERSON] et al. [60] presented a model where GAN is combined with CNN to enable flawless image and identify miniature surface defects. Another approach for a surface defect-generation adversarial network (SDGAN) was presented by [PERSON] et al. in [61]. The model was applied on accurate images to increase the dataset of defective images. For high-dimensional datasets, [PERSON] et al. in [62] presented a trained GAN-based model using a score function to increase the efficiency of defect detection. Another example for high-dimensional datasets was given in [63] by [PERSON] et al.