## 2 Literature Review
The authors trained a multi-headed neural network that utilizes automatically labeled images to detect deviations from optimal printing parameters. Automated data acquisition and labeling enabled the creation of an extensive and diverse 3D printing dataset with 1.2 million images from 192 different parts, which are tagged with printing parameters. The trained neural network, in conjunction with a control loop, enables real-time detection and rapid correction of various errors. The authors also create visualizations of the network predictions to provide insights into decision making [40]. [PERSON] et al. [41] present an automated quality assessment system for an FFF process using a proprietary real-time CNN model. The CNN model is trained and tested on images showing internal and surface defects. The performance of the model in detectingand evaluating defects in the AM process at different extruder speeds and temperatures is examined. The model achieves a precision of 94%, and the F-score, sensitivity and precision in real-time classification of the AM process quality are all above 75% for five different classes. The proposed model adds an automated, non-contact quality monitoring system to the AM process with the aim of stopping the AM process and eliminating time-consuming inspection of printed parts for internal defects [41]. [PERSON] et al. [42] developed an online quality monitoring system for material extrusion additive manufacturing by integrating laser scanning technology. This system captures real-time geometric data of each printed layer, enabling the detection of dimensional deviations and surface defects during the printing process [42]. In a layerwise monitoring methodology for material extrusion AM processes [PERSON] et al. [43] utilized a high-resolution blue laser line profilometer. This approach involves capturing point cloud data for each printed layer, enabling computation of layer height deviations and assessment of surface quality by detecting defects. The authors derived specific quality indexes, like average deviation of layer height, relative area of defects, and process stability slopes, to quantify layer height accuracy, defect occurrence and distribution, and overall process stability, respectively. The methodology's effectiveness is demonstrated in identifying process anomalies in real-time, thereby enhancing the reliability and repeatability of the AM process for producing functional parts [43]. [PERSON] et al. [44] explored the use of computer vision and deep learning to classify and predict the quality of multi-material jetting additive manufacturing processes. The study focuses on defects arising from improper droplet overlap settings. An automated classification system using custom CNNs trained on in-line surface measurements of printed layers is developed. Notably, models trained solely on first-layer images attained validation accuracies of 90%, indicating that analyzing just the initial layer suffices to predict the quality of subsequent layers [44]. [PERSON] et al. [45] focus on developing DL-based image processing techniques for real-time defect detection in additively manufactured continuous carbon fiber-reinforced polymers. While the paper does not specifically address co-extrusion processes or the use of laser profiler data, its emphasis on real-time defect detection through AI and deep learning methodologies offers valuable insights. The approaches discussed could potentially be adapted for in-line monitoring and quality assurance in various AM processes, including those involving co-extrusion and laser profiling techniques. Although a large amount of the available literature addresses the application of DL for quality monitoring in additive manufacturing AM, reviewing the available literature, it appears that there is a limited number of studies specifically addressing the application of DL for in-line monitoring of additive co-extrusion processes using laser profiler data.