## 6 Conclusions  
The novel prediction models of LHS-SVR and GA-SVR were established to characterize the hot flow behaviors of as-forged Ti-10V-2 Fe-3 Al alloy according to the experimental stress-strain data. The following conclusions were derived from the current study:  
1. The study ability and generalization ability of SVR rely on the three parameters \(C\), \(\gamma\) and \(\zeta\), especially the mutual impacts among them. An SVR with appropriate parameters \(C\), \(\gamma\) and \(\zeta\) will accurately study the stress-strain curves and appropriately ignore some singular points of stress-strain data to accord with the overall trend of the stress-strain curves.
2. The \(R\)-value and \(AARE\)-value between the training samples and fitted values of LHS-SVR and GA-SVR are 0.999997 and 0.0599255% and 0.999961 and 0.379903%, respectively, which show that LHS-SVR and GA-SVR can accurately learn the hot flow behaviors that accompany WH, DRV and DRX in wide temperature and strain rate intervals. The study abilities of these models were shown as follows in ascending order: ANN \(<\) GA-SVR \(<\) LHS-SVR.
3. After the comparisons of the generalization abilities of these models at the strain of 0.5, LHS-SVR and GA-SVR have larger \(R\)-values and lower \(AARE\)-values of 0.999975 and 0.074450% and 0.999923 and 0.529599%, respectively, which indicate that LHS-SVR and GA-SVR can accurately predict the highly non-linear flow behaviors. The generalization abilities of these models were shown as follows in ascending order: the mathematical regression model \(<\) ANN \(<\) GA-SVR \(<\) LHS-SVR.
4. The intelligence algorithm LHS-SVR can automatically calculate the parameter combinations in the search space to find the optimal value, which improves the computational efficiency compared to the mathematical regression model and ANN. Based on the selection, crossover and mutation operators, the GA-SVR can self-adaptively and dynamically search the optimal parameter combination, which greatly improves the computational efficiency compared to LHS-SVR. The modeling efficiencies of these models were shown as follows in ascending order: the mathematical regression model \(<\) ANN \(<\) LHS-SVR \(<\) GA-SVR.
5. Hot flow behaviors of materials at different conditions (such as different strain rates and temperatures) are highly non-linear and complicated; therefore, calculating stresses by interpolation means in finite element software is inaccurate. The flow behaviors outside experimental conditions were predicted by the well-trained LHS-SVR, which enhance the simulation precision of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles, such as improving the accuracy of finite element simulation results and improving processing maps.  
Figure 15: The relationship between the stroke and the load of the top die of experimental values, Scheme-A and Scheme-B.  
**Acknowledgments:** This work was supported by the National Key Technologies R & D Program of China (2012 ZX04010-081). The corresponding author appreciates the Chongqing Higher School Youth-Backbone Teacher Support Program.  
**Author Contributions:** [PERSON] and [PERSON] designed the experimental schemes; [PERSON] conducted the experiments; [PERSON] and [PERSON] analyzed the experimental data; [PERSON] offered analysis tools and some valuable suggestion; [PERSON] and [PERSON] wrote and revised this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.