## 3 Results and Discussion
### Feature Analysis
The identified feature of count above mean takes both cases into account. The CWT coefficient (CWT = continuous wavelet transformation) extracted from the melt-pool diameter measurement series is also considered significant. It is a way to represent the changing characteristics of non-stationary signals by decomposing the signal into wavelets. Wavelets are oscillations that appear in a short time section. Thus, stationary recurring parts of a signal can be detected. In the context of coatings, the CWT coefficient correlates with the uniformity of recurring subsections in the measurement data during the deposition of individual weld beads. It can be concluded that there is a correlation between the melt-pool diameter development of the individual beads and the overall quality of the coating. The entropy of the temperature data recorded by the pyrometer describes the complexity of the temperature curve. It is a measure of randomness for data series and contains information about the information gained with every new data point in the series with respect to the previously recorded points. Therefore, a high entropy of the temperature data indicates an unsteady temperature curve and, thus, irregularities in the whole build-up process. For coatings, all the extracted features are connected to laser power distribution and the general uniformity of the deposition process. Since laser power was already covered by features extracted from the measurement data, it was not included as an explicit feature. As \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Coating** & \\ \hline  & **Feature** & **Parameter/Sensor** \\ \hline
1 & Sum values & Laser scanner \\
2 & Count above mean & Emissions spectrometer \\
3 & Powder mass flow & Parameter \\
4 & CWT Coefficient & CCD Camera \\
5 & Entropy & Pyrometer \\
6 & Scan speed & Parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relevant features for coating categorization. for the weld beads, the quotient of laser power and powder mass flow defined the melted powder mass. Thus, powder mass flow was added as a feature. Moreover, the energy per unit length, defined as the ratio of laser power and scan speed, was a key factor for the deposition of coatings. The consideration of the process parameter of scan speed, therefore, seems reasonable. After variations in the significance of features from weld beads and coatings were observed, a different feature distribution was also obtained for the cubes. An overview of the nine relevant features is listed in Table 4.  
First of all, it can be noted that for additively manufactured components such as cubes, the significance of process parameters is no longer relevant. All the selected features are obtained from the measurement data series of the different sensors. The most important information is provided by the measurement data of the Plasmo sensor. As for the beads, change quantiles were selected as an important feature. In addition, the feature of time reversal contains information about the curve of the data series as well, since it is a measure of symmetry. Therefore, the emitted process light and its change over time is a good indicator for the quality of complex additive structures. Moreover, information about the build strategy is included in the measurement data and can be obtained from changes in the process light curve. For instance, the application of a frame structure can be distinguished from a filling zig-zag movement, where the afterglow of each weld bead was detected. The features extracted from the laser scanner are essential indicators for the quality of cubes, as well. Complexity, a Fourier coefficient and the partial autocorrelation are significant features. In the present case, complexity refers to the detected surface roughness of a cube. For example, a low complexity indicates only minor geometric irregularities in the coating of a cube and, thus, a good surface shape. The Fourier coefficient refers to the cross-sectional area of a cube.