## 3 Results and Discussion
### Data Preparation  
The importance of good data preparation is well known [19; 23], but there is no standardized procedure for data preparation yet [21]. It is often an iterative process to figure out how to create the best database for an ANN, especially in the early phase of model development. By applying the approach shown in Figure 2 on all the geometries, and by analyzing the obtained results, it could be verified as suitable for feature extraction and analysis in DED applications. The individual methods are explained in the following paragraphs. The first step of the data preparation is the import of measurement data as time series. Next, an automated feature extraction algorithm (tsfresh) is applied to obtain discrete values from the individual measurement series. It is already established in other applications [22]. Thus, about 800 features are calculated for each sensor and there are about 3200 feature values for each data point. Theoretically, a network could process this large amount of information, but it is not useful for practical applications. On the one hand, a high number of input values would make a larger data set necessary to identify correlations between input and output. On the other hand, redundant information is included, which would hinder and impair the training of the ANN. Therefore, a reduction in features is necessary. Simple and plausible physical and mathematical quantities that can be easily explained are desirable for the later evaluation. These include, for example, deviations from the mean value and linear trends. At the same time, however, the characteristics should reflect the relationship between the input and output variables as well as possible. In this context, complex quantities that are not obvious or easily physically explainable, such as Fourier coefficients, are also identified. With a univariate analysis, a simple procedure is applied to individually examine the influence of each feature on the target value and to calculate individual scores. The higher the score, the more significant the influence of the according feature on the output variable. Regarding the correlation statistics, a differentiation is made between numerical and categorical inputs: an ANOVA is carried out for numerical features, whereas Chi\({}^{2}\) is required for categorical features. This distinction is important for determining the significance of the respective feature. The significance indicates the strength of the relationship between the feature and the target variable. In the context of this work, a ranking of features was created, and the 10 highest-listed features were retained, while the rest were no longer  
Figure 3: Categorization of the different geometries. considered. To realize this kind of sortation, the desired feature quantity, as well as a specific significance level, can be used as a criterion. Next, the remaining features of all the measurement data series are brought together and compared in a correlation matrix. Such a matrix examines the correlation of two characteristics to each other without considering the target value. The correlation is represented by numbers from \(-1\) to \(+1\). For \(-1\) there is a strong anti-proportional correlation between two features, while for 1, the correlation is strongly proportional. The closer the number is to 0, the lower the correlation. If a considerable correlation is found between two features, the feature with the lower significance based on the univariate analysis is removed. Again, depending on the specific situation, the sortation can either be executed by defining a specific number of final features or by defining a threshold for the correlation value. In the context of this work, all correlations with an absolute value greater than 0.5 were reviewed and checked. The goal is to reduce the number of redundant features that provide little additional information gain. The correlation matrix is applied in two stages. First, the correlation matrix is applied to the features of a single sensor.