## 6 Discussion
### Findings
The effect of the investigated data augmentation techniques is minimal, although it is theoretically expected and cannot be completely dismissed in the results. For example, the model trained with the augmented dataset 4 in Table 8 shows the highest F1 score for the critical classes 'wire good' and 'wire bad', with F1 ('wire good') = 0.84 and F1 ('wire bad') = 0.80. For this reason, the augmented dataset 4 was used for the in-depth investigations with the fine-tuning models. Although no significant improvement was observed with the augmentation methods investigated, it does not necessarily imply that the increased training effort due to augmentation is useless. It is possible that variations learned through the augmented training images are not sufficiently included in the validation data used. If validation data is used that was printed with other materials or with other parameterizations or contains image sections of different sizes, the variations learned through augmentation may certainly prove useful. In any case, data augmentation is known as an appropriate tool for improving the generalization capability of models [61]; therefore, data augmentation techniques were included in the investigations. Since the augmentation techniques used did not have a strong improvement effect on the observed model classification performance, it can be assumed that the most appropriate augmentation techniques are not being used in the investigations. In general, augmentations must be well adapted to the task at hand in order to have a positive effect on model performance. In the described study, only rotations of 90\({}^{\circ}\) increments are realized. Rotations with smaller increments, for example, 45\({}^{\circ}\) or 30\({}^{\circ}\), may be better suited to improve the generalization capability of the trained model in the application under consideration. Mirroring (or flipping, respectively), random cropping, or random erasing of the images can also be considered as rather non-complex but well established and thus promising augmentation methods [37; 62; 63; 64]. In case more sophisticated augmentation methods than simple geometric operations such as rotation and flipping are needed, more elaborated augmentation methods such as style transfer [65; 66] and generative adversarial networks [61; 67], or combinations of standard and advanced augmentation methods [68], are available. It is also plausible to assume that the models are unable to learn the data effectively, resulting in inadequate classification. It is conceivable that the data requires deeper models. Although the difference between the VGG16 and ResNet50 models is not pronounced, a deeper model can lead to higher results. Examples of this are the EfficientNet ans ResNet models with different deeper architectures. However, the observation that both the VGG16-based pre-trained network and the deeper REsNet50-based pre-trained network exhibit barely any performance differences also allows the opposite assumption that for the given problem with comparatively low geometric complexity and low heterogeneity of the input dataset, the model architecture is already too deep to be optimally adapted to the given use case. In this respect, the limitation of the input dataset in terms of quantity, heterogeneity and balancedness of classes can probably be attributed to the highest weight in causing deficits in observed model performance. To fulfill the overall goal of process monitoring, the successfully demonstrated method needs to be trained through future experiments upon a larger and more heterogeneous dataset. In this study, a systematic hyperparameter optimization has deliberately been omitted, which implies the possibility of a potential improvement of the F1 score by such an operation. In particular, the number and size of filters and the stride should be systematically investigated, as these parameters have a significant impact on the performance of convolutional neural networks.