## 3 Results and Discussion
### Results of the Analysis of HiperFer-17 Cr2
#### 3.2.1 Low-Cycle Fatigue (LCF)
Note that the cyclic hardening potential is highly sensitive to not only the density and size of precipitation, but also its coherency with the surrounding material volume, and hence, the whole precipitation state (see [16]). Since the higher temperature leads to enhanced changes in precipitation, this might explain the more pronounced and earlier changes in \(\mid e_{II}\mid\) at this temperature. Note that changes in the density of imperfections of the material's lattice structure, e.g., dislocations and precipitations, influence the movement of the conduction electrons scattered at the lattice defects. These electrons are predominantly scattered by lattice vibrations (e.g., phonons) and by lattice defects. Given that the electrical conductivity of a material is proportional to the mobility of the conduction electrons, the changes in microstructure observed will result in a corresponding change in electrical conductivity. Moreover, the lattice imperfections impede the movement of the magnetic Bloch-walls and hence influence the magnetic permeability of the material. Depending on the size and distribution of the microstructural imperfections, different effects have an influence on the magnetic Bloch-wall movement: the foreign-body effect (in case of small precipitates), stress effect (in case of precipitates and dislocations) or stray field effect (in case of dislocations and big precipitates) [27]. Figure 6 demonstrates that the change in the features derived from the incremental permeability analysis \(DZmax\) (Figure 6b) is much smaller than the change in the features derived from the eddy current analysis \(W3Z\) and \(W10Z\) (Figure 6a,c). The change in \(W3Z\) and \(W10Z\) is about 30-50%, while the decrease in \(DZmax\) is approx. 15%. As the eddy current features reflect changes in both electrical conductivity and magnetic permeability [36], the results indicate that the main effect is caused by changes in the electrical conductivity. Given that the 3 MA-X8 generates a 21-dimensional feature space, manual correlation analysis becomes impractical. Therefore, ML methods are applied by utilizing multiparameter analysis to select micromagnetic features, which are most appropriate for the prediction of a targeted mechanical property. After that, the significant features are combined or'merged' into a regression approach. Various regression techniques (Table 3) are trained on the datasets collected by means of the 3 MA-X8, mechanical and microstructural characterization. In order to validate the regression methods, the specimens and the corresponding dataset are divided into two groups: training specimens, which include the corresponding training data, and test specimens, which include the corresponding test data. Approximately one-third of the entire set of samples (not only data points) is reserved  
Figure 6: Correlation of different 3 MA-X8 features with mechanical properties (a) \(\mid e_{II}\mid\), (b) \(\varepsilon_{a,p\_inter}\), and (c) \(HV10\) for HiperFer-17 Cr2 loaded under LCF conditions. for testing. It is important to mention that the extremes are not removed, as we aim to perform interpolation. Figure 7 presents the correlation of the reference values measured by mechanical characterization (x-axis) and the predicted values from the model (y-axis), with the green dashed line representing an \"ideal\" outcome. When considering all available 3 MA-X8 features, the regression regarding \(\mid\!e_{\Pi}\!\mid\) does not provide satisfactory results (Figure 7a), which is expected based on manual correlation analysis. The analysis of the individual 3 MA-X8 features indicates that only at 650 \({}^{\circ}\)C significant changes in micromagnetic features correlate with an increasing \(\mid\!e_{\Pi}\!\mid\) (Figure 6a), and hence, the prediction of \(\mid\!e_{\Pi}\!\mid\) for all temperatures is not feasible. In contrast, both \(\varepsilon_{a\textit{-}\textit{inter. }}\) and \(HV10\) (Figure 7b,c) can be better predicted.