## 5 Results and Discussion
### Strain-Life Behavior  
Table 5 presents a summary of the parameters obtained for Coffin-Manson and Basquin using the linear regression model. [PERSON] exhibited a better regression with a coefficient of determination, \(R^{2}\), equal to 0.984 against 0.705 with respect to [PERSON]'s model. Regarding the coefficients, the fatigue strength coefficient, \(\sigma^{\prime}_{f}\), was 1601.15 MPa, superior to \(\sigma_{uts}\), and the fatigue ductility coefficient \(\varepsilon^{\prime}_{f}\) was 47.80%, which is around double the monotonic strain at fracture, \(\varepsilon_{f}\). With respect to the exponent regressors, the fatigue strength exponent, \(b^{\prime}_{f}\), was equal to \(-\)0.093, whereas the fatigue ductility exponent, \(c^{\prime}_{f}\), was \(-\)0.684. According to the Universal Slopes method, USM, [54], the coefficients \(\sigma^{\prime}_{f}\) and \(\varepsilon^{\prime}_{f}\) could be correlated to the monotonic tensile properties, such that \(\sigma^{\prime}_{f}=0.623\sigma^{0.832}_{uts}E^{0.168}=1865.87\) MPa and \(\varepsilon^{\prime}_{f}=0.0196^{0.155}(\sigma_{uts}/E)^{-0.53}=0.2291\), with the exponents \(b^{\prime}_{f}\) and \(c^{\prime}_{f}\) being constants of \(-\)0.09 and \(-\)0.69, respectively. Comparing strain-life parameters estimated using the USM method, one can verify that the USM was non-conservative (16.53%) with respect to \(\sigma^{\prime}_{f}\); however, it was conservative in the estimation of \(\varepsilon^{\prime}_{f}\) (52.07%). With regards of the exponents, a good agreement was observed, with relative errors of only around 3.23 and 8.82% for \(b^{\prime}_{f}\) and \(c^{\prime}_{f}\), respectively. Recently, [PERSON], and [PERSON] [55] compared different estimation methods for the prediction of strain-life parameters. Accordingly, the outcomes, strain-life parameters, estimated regression based on the average values, and monotonic properties were very poor. In order to improve the estimation of strain-life parameters, [PERSON] and [PERSON] suggested an estimation model based on the median of individual parameters given the probability of distribution. The median model, MM, is suitable for a large range of materials, since it was developed based on 724 different steel grades, 81 aluminum alloys, and 15 titanium alloys. In a comparison with other estimation models, the MM model was shown to be more reliable for higher values of applied strain amplitude; however, for longer lifetimes, the MM model was less conservative than the USM model. Recently, the MM model was applied for Ti alloy components printed using electron beam melting, EBM, being shown to be conservative for longer lifetimes, but with a good fit with higher strain amplitude ranges [56].In the case of steels, the MM model suggests \(b^{\prime}_{f}=-0.09\), \(\varepsilon^{\prime}_{f}=0.45\), and \(c^{\prime}_{f}=-0.59\), and \(\sigma^{\prime}_{f}=1.5\sigma_{uts}\), which resulted in \(\sigma^{\prime}_{f}=1916.00\) MPa. From a comparison with the experimental strain-life parameters, \(\sigma^{\prime}_{f}\) remained non-conservative \begin{table}
\begin{tabular}{c c c c c c c c} \hline \(E\) (GPa) & \(\sigma_{y}\) (MPa) & \(K\) (MPa) & \(n\) & \(\sigma^{\prime}_{y}\) (MPa) & \(R^{\prime}_{y}\) (MPa) & \(K^{\prime}\) (MPa) & \(n^{\prime}\) \\ \hline
203.50 & 1042.0 & 1365.18 & 0.0513 & 578.5 & \(-\)463.5 & 1392.84 & 0.0954 \\ \hline \end{tabular}
\end{table} 
Table 4: Elasto-plastic parameters for monotonic and cyclic loading conditions. (19.66%); however, the estimation of \(\varepsilon^{\prime}_{f}\) was improved, with an error around 5.86%. The relative error of \(c^{\prime}_{f}\) was even higher (13.74%). Figure 7 illustrates the Morrow, Basquin, and Coffin-Mason models in green, red, and blue lines, respectively. An estimation of the value for the transition from monotonic to low-cycle fatigue resistance was calculated for a limit value of strain amplitude corresponding to 2 reversals, \(\varepsilon_{a,t}(2N_{f}=2)\), 30.49%.