## 3 Results and Discussion
### Analysis of the Used Data Sets  
Examining the data from the welding experiments, two specific cases regarding the general characteristics of the resulting deformation could be distinguished. During welding with low and medium energy input, the main shrinkage distortion in y-plane predominantly occurred at the top surface of the substrate, leading to positive distortion values in y-z-plane. At high energy inputs, the distortion values in y-z-plane were found to show comparatively low, and in particular cases, negative values. This may be explained by an increase in welding depth. Figure 5 compares the weld seam cross-section of a sample welded with low energy input; (a) shows comparatively low weld penetration depth with no visible formation of the weld root. In contrast, when welding with high energy input (b) a distinct formation of the weld root is visible. Depending on the size of the weld root, this leads to a significantly different shape of the specimen in y-z-plane, causing a change in the direction of main distortion. The quality of these generated data sets is decisive for the validity of the generated ANN and is examined in the following paragraphs. As an overview of the created data, four examples for the different slopes of the 48 data points are illustrated in Figure 6. There is no data for the bead count three as the experiments measured one, two, and four layers. Different exemplary progressions are shown, with the slope of the distortion staying positive in most cases. The noticeable exception is the lowest progression shown in light blue, where high energy input causes full penetration of the weld bed and results in negative distortion into the direction of the weld root. Five of these outlying experiments with a negative slope appeared in total, mostly at low welding speeds with a high wire feed. The data points showing a reduction of distortion seem inconsistent with the rest of the data and could have a negative impact on the accuracy of the ANNs. To filter out these outliers, different strategies were implemented using an unedited dataset as a point of reference: As a first filter strategy, the changes in distortion between the welding steps (see Figure 6) were examined and the datasets making up the worst 5% of data were removed before training and validating the ANNs. In contrast, in another strategy, the ANNs were trained with unedited data, removed the data with the greatest deviation from the fit of the created model with an implemented algorithm automatically and re-trained with the remaining data points. A third strategy was to remove all the datasets with the highest wire feed, as these appeared to be not suitable for applications in WAAM. For the standard ANN, a dataset using only the data for the maximum build-up of four layers was also used in order to model a possible improvement for the prediction of higher layer counts. The training and test split of the data was randomized for every new construction, with 80% of data used for training and 20% for testing. This construction of the ANNs was repeated ten times for each approach to choose the respective best networks in order to achieve a valid comparison. Figure 5: Weld seam geometry and resulting sample distortion for different parameter combinations: (**a**) low energy input, (**b**) high energy input. To assess the importance of the inputs and outputs for the ANNs, different combinations have also been tested. In total, three types of settings were evaluated for standard and enhanced ANNs, which are described as removal of data, as well as the combination of in- and outputs in Figure 7 (left). As an example, for the enhanced ANNs, one type of input with two types of outputs was implemented with four strategies for removal of outliers for a total of 8 examined enhanced ANN-structures. The results from these settings have been plotted in Figure 8 for the absolute test and training errors. Generally, the ANNs tend to be better with larger inputs, as seen in the graph in the top right.