## 4 Results
### Detailed Results
From the top to the bottom, the lines display the results for BAY, CAT, DTR, and KNN. From the left to the right, the columns display the results for CRTP, KvRTP, and FGRTP, respectively. On the other hand, the detailed results for the tensile strength problem are shown in Figures 11 and 12, Table 7. Analyzing the results for the CRTP, one can see that all of the models, with the exception of the SVR, achieved a poor performance. The support vector was the only one to achieve \(\tau\gg 0.5\) and a reasonable (\(R^{2}\)) despite not passing the Student's \(t\)-test. The same bad performance is also observed for the FGRTP case. None of the models passed the Student's \(t\)-test and the coefficients are far from values representing a good prediction performance. On the other hand, for the KvRTP, most of the models achieved good metrics, and the plots highlight a reasonable correlation, but none of them were able to pass the Student's \(t\)-test. Figure 10: Comparison for predicted vs. experimental Elastic modulus, GPa, part 2. From the top to the bottom, the lines display the results for LAS, RDG, RFR, and SVR. From the left to the right, the columns display the results for CRTP, KvRTP, and FGRTP, respectively. Figure 11: Predicted vs. experimental values for the Tensile strength, MPa, part 1. From the top to the bottom, the lines display the results for BAY, CAT, DTR, and KNN. From the left to the right, the columns display the results for CRTP, KvRTP, and FGRTP, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Fiber Type** & **Metric** & **SVR** & **DTR** & **RFR** & **CAT** & **KNN** & **RDG** & **LAS** & **BAY** \\ \hline  & \(r\) & 0.6819 & \(-\)0.0686 & \(-\)0.1237 & \(-\)0.0471 & -0.1073 & \(-\)0.0528 & \(-\)0.0528 & \(-\)0.0408 \\ CRTP & \(R^{2}\) & 0.4649 & 0.0047 & 0.0153 & 0.0022 & 0.0115 & 0.0028 & 0.0028 & 0.0017 \\  & Student’s \(t\)-test & - & Ok & Ok & Ok & - & - & - & - \\ \hline  & \(r\) & 0.0846 & \(-\)0.0206 & 0.0897 & 0.0886 & \(-\)0.0013 & \(-\)0.0325 & \(-\)0.0212 & \(-\)0.0450 \\ FGRTP & \(R^{2}\) & 0.0072 & 0.0044 & 0.0080 & 0.0078 & 0.0000 & 0.0011 & 0.0044 & 0.0020 \\  & Student’s \(t\)-test & - & - & - & - & - & - & - & - \\ \hline  & \(r\) & 0.8297 & 0.7266 & 0.8532 & 0.8146 & 0.7031 & 0.8235 & 0.8235 & 0.8215 \\ KvRTP & \(R^{2}\) & 0.6884 & 0.5279 & 0.7279 & 0.6635 & 0.4944 & 0.6782 & 0.6782 & 0.6749 \\  & Student’s \(t\)-test & - & - & - & - & - & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 7: Detailed results for the prediction of \(\sigma\). Unlike the commonly accepted ROM, the results here show no linear dependence. The nonlinearities induced by factors chosen by a designer, such as matrix filling or involuntary ones intrinsic to the fabrication process, such as porosity [37] or even the effect of the sample shape defined by the standard used on the test or the lack of adhesion between fiber and matrix [4] might not be well represented by the ML linear models. Finding the right combination of printing parameters will give the appropriate strength and stiffness needed for a part while maintaining geometry and density within requirements. Additionally, it helps maintain dimensional tolerances, which is crucial for applications that require high precision, reduced printing time, and material consumption, which translates into lower costs and Waste Reduction by minimizing waste produced during manufacturing. Figure 12: Predicted vs. experimental values for the Tensile strength, MPa, part 2. From the top to the bottom, the lines display the results for LAS, RDG, RFR, and SVR. From the left to the right, the columns display the results for CRTP, KvrTP, and FGRTP, respectively.