## 2 Literature Review  
With the rising availability of large data storage and increased computing power, it becomes further feasible to record and utilize large amounts of data. Deep learning has emerged as a particularly promising method for applications with complex correlations. Deep learning is also increasingly applied in computer vision to analyze images and videos and recognize specific objects. This technology is used in various areas such as autonomous vehicle technology, security technology and medical technology [30; 31; 32; 33]. Impressing with excellent recognition of correlations in space and time, e.g., in images or signal sequences, a frequently used algorithm is CNN. This is achieved by distributing weights to neuron connections and successively reducing the number of parameters in the forward progression through the artificial network architecture. A typical CNN consists of different layers, including convolutional layers, pooling layers and at least one dense layer. The algorithm is based on discrete convolution, where the input in two- or multi-dimensional matrices passes through one or more convolution matrices with filter kernels. It is worth noting that, depending on the problem, no standard solution exists; rather, the optimal architecture for a given problem must be found by trying out different approaches and structures [34]. A major challenge in training CNNs is the availability of labeled data. To ensure robust feature recognition, CNNs usually consist of a large number of deep layers. In order to develop a model, datasets with several thousand images per class are required. In practice, this is not always feasible if such large datasets are not available. The concept of transfer learning (TL) was therefore developed. TL involves using a model that has already been trained on an extensive dataset with millions of images. These images are typically not tailored to a specific application but represent a wide range of examples [35]. In its first few layers, which are close to the input, a CNN is designed to detect simple general features such as edges and corners. In the following layers, these features are combined to form high-level feature detectors. These are responsible for recognizing more complex and domain-specific features. Therefore, a model trained on general data can be used as a starting point for specific tasks to be trained. In this respect, robust models can be trained with limited datasets by truncating the fully connected (FC) layers of the base model and adding an application-specific head [36]. LeNet, also known as LeNet-5, is regarded as the starting point for the use of CNN for image recognition. In its basic form, the model consists of five layers, two of which are designed as convolution, followed by three FC layers. This model was initially used to recognize handwritten numbers [34]. AlexNet is an evolution of the LeNet architecture. It is considered a milestone in the development of CNN, in which it demonstrated the potential of deep networks. It consists of a total of eight layers, five of which are designed as convolution and three as FC layers [37]. With VGG, a distinction can be made between VGG-16 and VGG-19 depending on the type of implementation of this network. The suffix gives an overview of the number of layers used. VGG-16 consists of 13 convolution layers, 2 FC layers and 1 softmax output layer [38]. As the depth of CNN increases, a problem of convolution becomes apparent. With the layer depth, the amount of information still available from input at deep layers drops excessively. The ResNet architecture therefore does not exclusively consist of simple convolution layers but is also capable of forwarding full-size input information to deep layers of the network via residual connections [39]. A study by [PERSON] and [PERSON] [40] deals with the application of CNN in the monitoring of material extrusion as an additive manufacturing method.