## 4 Discussion  
### Global Model Results  
Figure 13 describes the datasets in descending order of their global rank. First, using the rank and the ratio of the mean MAE to the mean target feature value, the five hypotheses described in Section 1.4 are evaluated. The first hypothesis claims that aggregated features improve the model quality due to their hidden information about the conduction of experiments. DS 1 and DS 2 versus DS 3 and DS 4 differ in the use of the aggregated composition features. DS 1 scores a lower mean RMSE than DS 3, but DS 4 scores better than DS 2. Looking at the exact values, the use of the extended features is far more significant than the use of the aggregated features. They have no significant effect on the model quality, refuting the hypothesis. The second hypothesis claims that using the acceleration data improve the model quality. The best dataset (DS 9) uses the mfcc features calculated from the acceleration data. The relative difference in mean RMSE is 5.1% to the base dataset (DS 1), which scores rank 2. As seen in Figure 16e, DS 11 scores best when the time-based signals are dropped. DS 5, 6, 7, and 8 contain the FFT features and are reliably the worst for both base datasets. The high number of features was too complex for the number of datapoints, even after complexity reduction. Figure 16e shows the SHAP plot for the best model using FFT features at a 4% filter fraction, which still results in 193 remaining features. It contains the flexural and compressive strengths and the relative masses at the start of the interval. The other listed features are FFT bins without a recognizable pattern. For the datasets containing the extended features, DS 12 scores best using the time-based signal data. Figure 16f shows its most important features, which comprise the base features and two evaluation features of the maximum negative acceleration. In summary, the signal data contain, in the best case, some information and, in the worst case, mostly noise. Once again, this can be explained with constant experimental processes during data collection. Without significant variation, those features contain mostly noise. The 5.1% improvement for DS 9 is too low to be sure whether the improvement comes from more information or model training. The third hypothesis claims varying model qualities for different signal-processing approaches. As just explained, the differences between the mfcc and time-based features are small. The FFT features yield worse models due to their high count and complexity. The fourth hypothesis states that including all features will reduce the model quality despite the use of complexity reduction. This statement is true. DS 5 and DS 6 yield for both base datasets the worst model quality. More features without more information merely introduce noise to the data. The fifth hypothesis states that lower data complexity results in a better model score. This hypothesis can be confirmed. Only DS 3 achieved its lowest mean RMSE with a 100% filter fraction. All other datasets used filter fractions between 1 and 90%. The results and filter fractions in Figure 13 show that all datasets containing extended features profited from a lower filter fraction than those only containing the base features. The remaining most important features for the extended datasets are only the base features. Only the strengths at 400 \({}^{\circ}\)C are used in three out of six datasets, but these are in the lower third of important features in all cases. The datasets using the FFT features profited significantly from the data complexity reduction, as seen in Figure 21. The datasets containing the extended features yielded worse models than the less complex datasets. However, the number of datapoints in this last comparison is not constant. The number of datapoints in the datasets with the extended features was too small to achieve a low mean RMSE.