## 3 Results and Discussion
### Feature Analysis  
The feature analysis performed on the measurement data of the weld beads resulted in a total of 7 input variables. An overview of the identified features, ranked by significance, is shown in Table 2.  
Two features of the laser scanner have the highest information content: the longest strike above mean and the count below mean. The first feature indicates the length of the longest sequence of values that are higher than the mean value of the time series. The second feature describes the number of values in a measuring sequence that are smaller to the mean value. Therefore, both values reflect whether a weld bead shows a uniform course. Possible defects such as an increase at the ends or a dip in the middle of the weld bead can be identified by the selected features. The uniformity of a weld bead can also be described by the course of the ratio between the bead width and bead height; these are target values that are often examined as quality characteristics [13]. The median temperature measured by the pyrometer is also of high significance. If it is too low, not enough powder is melted. In contrast, excessive power is applied if the temperature exceeds a certain limit. This can, on the one hand, lead to tarnishing, and on the other hand, to powder melting irregularly. The quotient of laser power and powder mass flow defines the melted powder mass. Thus, both process parameters are considered to be important features. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Weld Bead** & \\ \hline  & **Feature** & **Parameter/Sensor** \\ \hline
1 & Longest strike above mean & Laser scanner \\
2 & Count below mean & Laser scanner \\
3 & Median & Pyrometer \\
4 & Change quantiles & Emission spectrometer \\
5 & Laser Power & Parameter \\
6 & Linear Trend & Emission spectrometer \\
7 & Powder mass flow & Parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relevant features for weld bead categorization. For the Plasmo sensor system, the change quantiles and linear trend are significant features. Change quantiles describes the average change of measured values in a certain frequency range. The linear trend represents a linear regression of the measurement series and indicates that the overall trend of measured values is important. Both features can be explained in physical terms by considering the sensor's functionality; changes in the emitted process light indicate irregularities in the process. The measuring data of the process camera were not considered due to low significance. The reason for this is the comparatively short welding time for a weld bead. Moreover, at the beginning and end of a welding process, a process glow occurs that falsifies the results of the applied image-processing algorithm. A substantial lengthening of the weld bead would be inappropriate given the effort. The relevance of features depends on the geometry that is built up. For the coatings, the feature analysis identified different significant features than for the weld beads. The six relevant features of the coatings are listed in Table 3.  
As with the weld beads before, the laser scanner also provides the most important information for coatings. In this case, sum values are the integral over the cross-sectional area of the coating. The feature contains information about the size and height and, thus, the dimension of the coating. One explanation for the strong quality correlation is the non-uniform deposition of the coatings if their heights exceed a certain limit. The second most significant feature for coatings was extracted from the measurement data of the Plasmo sensor system. The count above mean indicates the number of values in the measurement series that are higher than the average value. These amplitudes are caused by increased light reflections and process lightning. They either occur if the introduced laser power is too high or if the powder is melted irregularly. In both cases, poor coatings with a strong tarnis or wavy surface are the consequence.