## 4 Experiments
### Data Generation and Data Aquisition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Line** & \multicolumn{3}{c}{**Content (Example)**} \\ \hline
1 & *** & 11.10.2023 & 13:58:21 & & & \\
2 & *** & Buffer Width: 1272 & & & & \\
3 & *** & Buffer Length: 1058 & & & & \\
4 & *** & Amount of Points: 1,345,776 & & & & \\
5 & *** & x, y, z & & & & & \\ // & x & y & z & x & y & z & \\
6 & \(-\)38.4120 & \(-\)34.9800 & 29.9725 & \(-\)38.3112 & \(-\)34.9800 & 29.8765 &... \\
7 & \(-\)38.4120 & \(-\)34.9400 & 29.8525 & \(-\)38.316 & \(-\)34.9118 & 29.8525 &... \\... &... &... &... &... &... &... &... \\
1058 & \(-\)38.4104 & \(-\)31.8692 & 29.9785 & \(-\)38.356 & \(-\)31.8692 & 29.9485 &... \\ \hline \end{tabular}  
* Sequence *** serves as indicator for header information: Date and time, the packet width, the packet length and the number of all points (provided by scan software and carried through as such).  
\end{table} 
Table 3: Basic structure of a CSV file issued subsequent to the laser line scan. In order to extract a proper amount of information from the image files, the output files are divided into nine different patches (see Figure 4d). The splitting is convenient as common transfer learning models do not operate with 600 \(\times\) 600 pixel data, as given by the images prior to splitting, and in addition, a higher number of image instances are made available for the learning approach, which can always be beneficial in training ANN models. A frequently used image resolution in ANN applications is 224 \(\times\) 224 pixels. Splitting allows to increase the total number of samples and therefore also the number of samples per class. However, it is important to verify and adjust the class labels of the cropped image tiles according to the visual information in the particular image patch. The output dataset consists of the processed scans. Not all scans can be used, as some contain a failure or the quality is insufficient. After processing and splitting the individual scans, the dataset has the extent depicted in Table 4. It can be seen that the dataset is unbalanced, as the classes'solid' and 'infill' are comparatively overrepresented, which is due to the fact that'solid' and 'infill' layers are consistently printed during printing. The class 'infill' is overrepresented in particular with a share of 44.6% of the total dataset. It is technically not feasible to reinforce each layer with wire, so that regular infill layers are constantly printed in between. If a print fails, this is typically attributable to the reinforcement layers, but this does not apply to the 'infill' and'solid' layers, where issues occur less frequently. The target of 100 images for the class 'wire good' before data processing was not fulfilled, which is a result of technical difficulties. In order to examine the influence of the dataset size on the performance of the models, the dataset is extended using augmentation. This involves using the rotation technique for augmentation; the initial data are rotated by 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\). These angles are beneficial  
Figure 4: **(a)** Raw data file: all height measurement values recorded with laser line scanner in a CSV. (b) Area of interest: area specified via the interface. (c) Pre-processed data: output file before splitting into patches. (d) Processed output: split patches (numbers 1–9) in format 224 \(\times\) 224 pixels appropriate for model learning. as there is no loss of information that would otherwise occur with other rotation angles. After the procedure, the dataset volume is four times the size as before (see Table 4). For the sake of unambiguity, an extension is added to each file, consisting of the original file name suffixed by the rotation angle. In order not to falsify the results of the dataset, the data used in the initial dataset (No. 1) are augmented in their respective folders (training, validation, and testing, in a ratio of 0.7/0.2/0.1, respectively). This preserves the comparability of the results of the models among each other.