## 3 Results and Discussion
### Feature Analysis
In general, it describes the degree of recurrence of trigonometric functions in the data series. In this particular case, it refers to the pattern of weld beads in the top layer and their distinctiveness. In contrast, partial autocorrelation identifies the dependency between values of a measurement series at different times, and provides information about the predictability of a data point based on previous or subsequent points. Since this feature was extracted from the total height of the cubes, it indicates their overall geometric structure. For additive components, dimensional accuracy is of high importance. Thus, it is reasonable that three different features, related to the surface roughness, cross-section and absolute height of the cubes, are selected. For the temperature measurement data, a Fourier coefficient and change quantiles are relevant. The Fourier coefficient refers to a pattern structure in the temperature curve that is related to recurring parts due to the repetitive application of individual tracks and layers. Furthermore, a high change quantile value indicates strong temperature fluctuations and, therefore, an irregular process. Moreover, the part quality is influenced by the build-up strategy. For instance, if the cooling time between layers is below a certain threshold, overheating during the build-up can occur. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cube** & \\ \hline  & **Feature** & **Parameter/Sensor** \\ \hline
1 & Change quantiles & Emission spectrometer \\
2 & Complexity & Laser scanner \\
3 & Fourier coefficient & Laser scanner \\
4 & Time reversal & Emission spectrometer \\
5 & Partial autocorrelation & Laser scanner \\
6 & Fourier coefficient & Pyrometer \\
7 & Number crossing 1 & CCD Camera \\
8 & Change quantiles & Pyrometer \\
9 & Linear trend & CCD Camera \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relevant features for cube categorization. The process camera with the features of number crossing 1 and linear trend also provides important information on the quality of the cubes. Number crossing 1 defines how many times the curve of the melt-pool size undercuts the value 1 or approaches 0. In this context, that undercutting can be equated to no melt pool being detected. This can be due to a deposition pause between single weld beads or layers. In this case, number crossing 1 is a measure of the number of individual weld beads in the whole cube. Another possible reason is a strong process irregularity that prevents the image processing algorithm from detecting a melt pool. In contrast, the linear trend considers the melt-pool diameter change over time. If the size of the weld pool increases, the weld bead geometry changes, which has a direct effect on the geometry of the cube. If the melt-pool diameter decreases, the distance between the component and the nozzle increases, not enough energy is introduced, and the powder is no longer melted. Cubes with significantly lower overall height and a smoothed surface are the result. In summary, the extracted features vary depending on the geometry. The more complex the manufactured structures are, the more important the measurement data become. Thus, it can be concluded that in the case of additive components such as cubes, the significance of process parameters is extremely low. When compared to the extracted and analyzed features, they no longer provide any significant information for the ANN. In the following section, the importance of selected features for quality prediction will be explained.