## 3 Results and Discussion
### Prediction of Nominal Stress and Comparison of Models' Performances  
The prediction of Nominal stress (maximal nominal stress) was also performed based only on the part positioning in the build chamber and STL model properties (number of mesh triangles, surface, and volume). However, compared to results obtained for Tensile Modulus, a number of successful models for prediction of Nominal Stress have higher prediction accuracies. While models in the XYZ and XZY orientations show similar behavior as for Tensile Modulus, the ZYX and Angle orientations have even better prediction accuracy (0.902 and 0.906, respectively). In addition, an interesting phenomenon can be observed for Angle orientation. Since a number of data points for this orientation are smaller than in the XYZ orientation, model performance is significantly better. This can be explained by looking at the linear regression models. In this case, the prediction of Nominal Stress in the Angle orientation is even possible with the use of a linear model, which has a relatively high prediction accuracy--0.867 out of 1. This means that a combination of part positioning and STL model properties has a linear correlation with Nominal Stress in Angle orientation. Therefore, the application of more advanced methods results in improved model performance with higher prediction accuracy (like AdaBoost regressor in Table 6). The prediction of Nominal Stress in the ZYX orientation has similar results to Tensile Modulus, and the best model is based on the GradientBoost Regressor. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**\# Data Points**} & \multirow{2}{*}{**Orientation**} & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**GBR**} & \multicolumn{2}{c}{**DTR**} & \multicolumn{2}{c}{**ABR**} \\  & & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** \\ \hline
110 & XYZ & 0.393 & 2.093 & 0.197 & 2.768 & 0.173 & 2.852 & 0.372 & 2.165 \\
40 & XZY & \(-\)0.645 & 0.827 & \(-\)0.013 & 0.509 & \(-\)0.043 & 0.525 & 0.50 & 0.251 \\
142 & ZYX & 0.252 & 15.983 & **0.902** & **2.102** & 0.893 & 2.293 & 0.843 & 3.356 \\
74 & Angle & 0.867 & 0.527 & 0.855 & 0.578 & 0.839 & 0.642 & **0.906** & **0.373** \\ \hline
325 & All & 0.763 & 10.193 & **0.964** & **1.537** & 0.963 & 1.609 & 0.937 & 2.689 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction of Nominal Stress with the help of machine learning techniques. Linear means linear regression models, GBR stands for Gradient Boost Regressor, DTR for Decision Tree Regressor, and ABR for AdaBoost Regressor. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**\# Data Points**} & \multirow{2}{*}{**Orientation**} & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**GBR**} & \multicolumn{2}{c}{**DTR**} & \multicolumn{2}{c}{**ABR**} \\  & & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** & \(\mathbf{R^{2}}\) & **MSE** \\ \hline
110 & XYZ & 0.129 & 602.945 & 0.272 & 503.781 & \(-\)0.76 & 1219.019 & 0.404 & 412.905 \\
40 & XZY & \(-\)0.927 & 1421.147 & \(-\)2.297 & 2431.052 & \(-\)2.313 & 2442.847 & \(-\)2.102 & 2286.854 \\
142 & ZYX & 0.48 & 4151.161 & **0.809** & **1524.994** & 0.764 & 1886.726 & 0.801 & 1586.234 \\
74 & Angle & 0.03 & 1193.589 & \(-\)0.159 & 1426.052 & \(-\)0.469 & 1808.483 & 0.264 & 905.402 \\ \hline
325 & All & 0.528 & 3876.678 & **0.888** & **916.721** & 0.819 & 1488.903 & 0.879 & 995.159 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction of Tensile Modulus with the help of machine learning techniques. Linear means linear regression models, GBR stands for Gradient Boost Regressor, DTR for Decision Tree Regressor, and ABR for AdaBoost Regressor. As can be seen in Table 6, the best model with the highest prediction accuracy and the lowest MSE corresponds to the case when gathered data is not separated into orientation-based groups.