## 1 Introduction
### Defect Detection and Prediction by the Application of Deep Learning  
The use of artificial intelligence, particularly deep-learning algorithms, to detect or predict processing errors has already been scientifically investigated in several places. Deep Learning (DL) has become one of the most widely used computational approaches in machine learning (ML) and achieves excellent results at a variety of complex computer vision tasks [37]. The following approaches address the ability of implementing DL into data analysis to detect and even predict failures in powder bed fusion of polymers. A previously published work revealed reproducible results in curling defect detection by thermal imaging and convolutional neural networks (CNN) [38]. Curling defects were manually induced by adjusting certain process parameters, and thermography was used to highlight related temperature distributions. The extracted frames of accurate and defective processes served as a basis to train a number of CNNs. The CNN achieved an average curling defect detection accuracy of 98.54%. According to [18], [PERSON] and [PERSON] attained similar accuracy results of 95.8% using the VGG16 CNN and recordings by a simple high-definition camera system. Various powder bed defects were analyzed and predicted by the CNN. A further approach of detecting powder bed defects in PBF-LB/P by applying a digital camera system and deep residual neural networks was suggested by [PERSON] et al. [39]. Three different powder bed defects, curling, part shifting, and powder short feed, were artificially generated by varying the process conditions, using two different PBF-LB/P powder types. This method exhibited high accuracy rates of up to 96% in defect detection and was able to cope with image blurring and geometrical distortion. In [40], an AI-drivenapproach of using automated build part characterization to detect and forecast printing errors in PBF-LB/P was presented by [PERSON] et al. To investigate the problem of error prediction, a combined method of unsupervised pseudo-labelling (autoencoder-based clustering) and supervised deep learning (ResNet50) was applied. Based on grayscale-camera pictures, an error detection accuracy of more than 93% was achieved. Using the cox proportional hazards (CPH) model to predict the remaining time until error, statistically significant forecasts (\(p<0.005\)) of the remaining build time without failure were achieved.  
Beside the practical approaches of process monitoring and defect detection in powder bed fusion of polymers summarized here, further sensor concepts such as inline coherent imaging [41], enhanced phase measuring profilometry [42], multi sensor fusion [43; 44], and acoustic approaches [45] were investigated for selective laser melting of metals (PBF-LB/M). The application of DL provides an effective method of monitoring the process of powder bed fusion of polymers. However, the images used to train and evaluate a CNN require a very large amount of data and preparation is necessary to achieve the above results. Alternative data as provided by laser profilometry has not yet been processed by DL. The approaches outlined here clearly demonstrate the effective use of thermal imaging in PBF-LB/P in terms of part properties, in situ process control, and further analysis of process steps, but the data acquired does not allow direct correlation with geometric height values. The actual detection of curling errors using thermal imaging technology was not part of the investigations presented and has not been able to be realized so far due to a lack of 3D information. Additional measurement methods, such as laser profilometry, are required to verify the suspected failures at the powder bed.