## 1 Introduction  
In the future vision of industry, intelligent machines coordinate manufacturing processes independently, and autonomous devices perform most of the human operators' routine tasks. Networking will take place both within factories and across company boundaries. Digital networking combined with additive manufacturing technologies enables new production processes, business models and products [1, 2, 3]. Thanks to continuous monitoring of machine statuses, potential defects can be detected quickly to prevent breakdowns and costly downtimes [4, 5, 6]. Additive manufacturing processes excel by combining maximum design freedom with the best efficiency for the production of small series [7, 8, 9]. On the other hand, improvements in dimensional accuracy, load-bearing capacity of processable materials and reproducibility of production quality still need to be achieved in order to make additive manufacturing processes robust for industrial production [10, 11, 12]. Reinforced plastic composite parts are recognized by structural characteristics tailored to the load profile and therefore offer particularly high lightweight construction capabilities [13; 14; 15]. Compared to established manufacturing processes with the necessary toolmaking and the comparatively complex and correspondingly costly production of plastic composite components, additive manufacturing of reinforced plastic components can be expected to yield major gains in manufacturing efficiency [16; 17; 18]. Multi-material 3D printing can combine the advantages of additive manufacturing with the benefits of fiber-reinforced plastic composite components. Quality assurance constitutes a key factor in the use of wire-reinforced 3D printing processes, as the quality of the printed parts significantly influences the component properties and reliability in various applications [19; 20; 21]. However, traditional methods of quality control, such as visual inspection, face limitations, especially when it comes to detecting and evaluating complex structures and subtle defects inside a component. In this study, routines are developed and evaluated facilitating in-line monitoring for a thermoplastic co-extrusion printing process utilizing data from a laser profiler, scanning each printed layer and recording corresponding height profiles. Computer vision is an area of artificial intelligence (AI) that enables computers to extract relevant information from digital images, videos and other visual inputs and, as a result, take action or make recommendations [22]. When it comes to large volumes of data, as typically can arise in ongoing production processes, deep learning (DL) methods are a particularly promising way of leveraging conventional domain-specific process knowledge for automated production monitoring solutions [23; 24; 25]. Artificial neural networks (ANNs) can significantly enhance process monitoring in industrial processes through their ability to learn patterns, adapt to complex data, and make predictions [26; 27]. By leveraging these capabilities, ANNs improve decision-making, efficiency, and reliability in industrial process monitoring [28; 29]. Therefore, this study investigates and evaluates the application of artificial neural networks for quality control of wire-reinforced 3D printing processes. The focus is on the development and implementation of deep learning models that can accurately identify and characterize defects and quality anomalies in printed components. Test components in two variants are produced, and data acquisition is carried out with a laser profiler. The collected data are used to train deep neural networks to classify the printed layers into four different classes and identify those layers with insufficient quality. The stages of data acquisition, data preparation, modeling and evaluation are documented in detail. Data acquisition essentially describes the design and printing of test components, scanning with the laser profiler and importing the recorded data.