## 2 Background and Methodology
by investigating a new anomaly method based on searching for a good representation of this sample in the latent space of the generator. The final category of unsupervised CNN models for defect detection is the hybrid variant. A generic method for automated surface defect detection based on a bilinear model has been proposed by [PERSON] et al. in [64] by extracting the features globally and locally, applying subnetworks based on visual geometry and labelled Double-VGG16. Another approach of a hybrid unsupervised CNN model was presented by [PERSON] et al. [65] for automatic defect detection in material surfaces. The study included a two-stage deep learning scheme for pixel-wise defect detection on textured surfaces using two CycleGAN models and a U-Net semantic network. The previous studies showed that the use of deep learning is well suited for industrial manufacturing purposes in order to classify and identify both external and internal defects. The targeted use of CNN in additive manufacturing, especially in powder bed fusion, has already been discussed in several applications and has shown very good results in process monitoring, defect detection, and the prediction of component quality [47]. What is striking, however, is that most of the research is being carried out in the field of powder bed fusion of metals, and there are only a few cases of CNN being applied to the manufacturing of polymers. This could be due both to the nature of the defects, which are more visible and occur in greater numbers in the metal fabrication sector [33; 34], and presumably to the interest from the manufacturing industry [66]. Our previous approach [35] showed good results in detecting curling defects on the same PBF-LB/P system using thermal imaging and defect classification using a VGG-16 CNN. An average accuracy of 98.54% for the detection of curling defects was achieved and the results encouraged the effective use of DL for the non-destructive, in situ quality control of powder bed fusion processes. Another study by [PERSON] and [PERSON] [67] demonstratedthe application of non-destructive defect detection in the field of powder bed fusion of polymers using complex transfer learning (TL) methods. Their approach used a VGG16 and Xception CNN model with pre-trained weights from the ImageNet dataset as the initialization. In [68], [PERSON] et al. demonstrated a DL defect detection system called \"SLS-ResNet\" to detect defects such as curling, part shifting caused by curling, and powder short feed on a DTM Sinterstation 2500 Plus PBF-LB/P system. Unfortunately, no further information on defect generation was given. Another approach on the same Sinterstation was presented by [PERSON] et al. in [69] to detect the same defects as in the previous study, but in this case, it was explained in detail how certain parameters such as process temperature were manipulated to create the defects. In [70], [PERSON] et al. showed a method for the in-line assessment of part porosity to detect limitations in the reproducibility of manufactured parts by using deep residual neural networks. Their study used the same EOS PBF-LB/P system as the current study. [PERSON] et al. [23] showed the influence of component positioning in the installation space of an EOS P 395 PBF-LB/P system on part dimension accuracy. This work aimed to predict the scaling ratio for each part separately, depending on its placement, orientation, and CAD characteristics, using artificial neural networks (ANN) such as Multi-Layer Perceptron (MLP) and CNN.