## 6 Discussion
### Findings  
In particular, in the most technically interesting and probably most application-critical areas of the 'wire good' and 'wire bad' classes, the prediction results of the developed models showed weaknesses, with F1 scores below 85% even for the best models in the study. The probable main reasons are to be found in the following categories:  
* Data input;
* Model structure;
* Hyperparameter settings. The experiments illustrate that the maximum F1 score is achieved by using pre-trained models. However, such models are not only more complex but also not inherently compatible with the initial data from the source. In particular, the performance of custom CNNs is highly dependent on the architecture and requires detailed investigation. No model-dataset combination achieves an F1 score of over 85% for the 'wire bad' class. This is particularly problematic for components with multiple reinforcement layers, as an incorrect classification of just one layer is bound to result in the printing process being aborted. The results in Table 11 indicate that fine-tuning does not yield a significant improvement. It is possible to optimize the hyperparameters of the fine-tuning, but a significant increase in the results is not expected. For example, the different number of enabled base model layers as well as the learning rate and batch size did not have any influence on the classification quality. A comparison with the results of all models in Table 8 suggests that a significant improvement cannot be expected. Various causes are conceivable:  
* Insufficient quality of the dataset;
* Insufficient scope of the data;
* Differences among the classes are too small to distinguish significantly from each other. The lack of quality of the dataset provides a plausible explanation for the performance of the models, which in part requires improvement. It is striking that all models exhibit a similar maximum F1 score for the 'wire bad' class, each of which ranges closely to the other. This suggests that the models are poor at correctly classifying some images. It is conceivable that some images are incorrectly labeled or contain excessive noise. The quality of the images captured by the laser scanner depends heavily on ambient conditions. Excessive ambient light or surfaces with excessive reflection can severely distort the images. Prior to the start of the experiments, the dataset was checked for poor records and totally invalid records were excluded. However, measurement artifacts are also to be expected in a prospective real application scenario; therefore, the recorded noisy data does not necessarily need to be an unsuitable basis for model development. As a consequence, a selection of noise recordings characterized by inferior quality were retained in the dataset. This was carried out deliberately to give the models a stronger generalization capability. It can be assumed that these records are responsible for the stagnation of the F1 scores. This also shows the importance of the correct setting for the laser line scanner. In subsequent experiments, it is conceivable to darken the print chamber in order to eliminate ambient light components. A revision of the dataset used in this work and the use of explicit images only for the training of the models are also an alternative. The reason for the distribution of the data in the dataset of the study can be attributed to the characteristics of the printing process utilized in the experiments. As a matter of procedure, more images are recorded from the'solid' and 'infill' areas as opposed to the 'wire good' and 'wire bad' areas. Consideration can be given to removing data from the'solid' and 'infill' classes from the dataset in order to restore parity. Because adisparity between the classes equivalent to the experimental results is to be expected in a real application scenario, it was decided to retain the data distribution resulting from the printing experiments'.