## 3 Results and Discussion
### Impact of ECAP Processing Factors
#### 3.2.3 Tensile Properties
Substantial refining of grains (as depicted in Figures 3 and 4) also resulted in a mechanism of strengthening for the grain boundary, which enhanced the Mg billets' mechanical properties, as corroborated by [78]. Consequently, \(\sigma_{\rm u}\) was found to be greater for ECAP Mg billets with \(\phi=\) 90\({}^{\circ}\) than for those with \(\phi=\) 120\({}^{\circ}\) as a result of the smaller grains obtained from the former. The reduced grain size caused an augmentation in grain boundary area per unit volume, which in turn strengthened the material and caused a minimization of ductility, as clearly shown at \(\phi=\) 90\({}^{\circ}\) with the number of passes augmentation, where earlier studies [75] revealed identical trends. However, the 120\({}^{\circ}\)-die showed more ductility than the 90\({}^{\circ}\)-die, which can be explained by the decreased strain observed while utilizing the 120\({}^{\circ}\)-die, particularly in comparison to the 90\({}^{\circ}\)-die. Thereby, the 120\({}^{\circ}\)-die revealed bimodal and dynamic recrystallization grains (DRX) in the bulk materials, which helped increase ductility [79]. According to studies [80; 81], fine grains with a size of less than 5 um kept their elevated strength, whereas coarser grains with a size of roughly 20 um offered strainhardening to sustain the transformation to enormous strains. Thus, the ductility of Mg specimens obtained from ECAP was enhanced by the specific textures that prefer grain sizes in the region of 10-20 \(\upmu\)m and basal slip, as found in [75]. Moreover, the bimodal grain structure may account for the reduced difficulty of moments of dislocation seen in ECAP billets relative to those in the AA condition [79]. Similar findings of improving the tensile properties due to a significant grain refinement were reported in [82]. Figures 8 and 9 show the results of ML modeling \(\sigma_{\text{u}}\) and \(\text{D}_{\text{u}}\) of the Mg alloy. The results revealed a significant relationship between the predicted and experimentally-driven datasets. The RMSE and R\({}^{2}\) scores depicted in Table 5 revealed that the majority of the selected methods performed satisfactorily with data training and testing. It is worth noting that SVR and GPR performed the best for obtaining a model representing the process variables, which were assessed using both the RMSE and R\({}^{2}\) score. The LR was not able to model the data trends, whereas both the SVR and GPR successfully did, which was due to their inherent advantages when dealing with nonlinear models. For these models, the R\({}^{2}\) score range was between 0.79 and 0.99 for the training dataset and 0.72 and 0.92 for the testing dataset. Figure 8: (**a**) Predicted versus experimental \(\omega\) values examined for the training and the testing datasets. (**b**) Predicted \(\sigma\)u values versus sample indices for predicted train, predicted test, and experimental dataset. Figure 9: (**a**) Predicted versus experimental ductility values examined for the training and the testing datasets. (**b**) Predicted ductility values versus sample indices for predicted train, predicted test, and experimental dataset.