## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**F/\(\delta\) FEA and ML (N/mm)**} & \\ \hline
**Lattice-Flexure** & **GPR** & \begin{tabular}{c} **\%E** \\ **GPR** \\ \end{tabular} & **ANN1** & \
\begin{tabular}{c} **\%E** \\ **ANN1** \\ \end{tabular} & **SVM** &
\begin{tabular}{c} **\%E** \\ **SVM** \\ \end{tabular} & **ANN2** \\ \hline RH-5.5-0\({}^{\circ}\) & 0.0847 & 0.02\% & 0.0848 & 0.099\% & 0.0815 & 3.80\% & 0.0847 & 0.019\% \\ RH-5.5-20\({}^{\circ}\) & 0.0671 & 0.00\% & 0.0671 & 0.001\% & 0.0629 & 6.26\% & 0.0671 & 0.001\% \\ RH-5.5-40\({}^{\circ}\) & 0.0409 & 0.08\% & 0.0401 & 1.880\% & 0.0423 & 3.50\% & 0.0409 & 0.078\% \\ RH-5.5-60\({}^{\circ}\) & 0.0348 & 0.09\% & 0.0367 & 5.364\% & 0.0363 & 4.22\% & 0.0348 & 0.091\% \\ RH-5.5-80\({}^{\circ}\) & 0.0407 & 0.01\% & 0.0398 & 2.224\% & 0.0464 & 13.99\% & 0.0407 & 0.013\% \\ HH-3.5-0\({}^{\circ}\) & 0.1078 & 0.00\% & 0.107 & 0.746\% & 0.105 & 2.60\% & 0.1078 & 0.004\% \\ HH-3.5-20\({}^{\circ}\) & 0.0969 & 0.03\% & 0.0988 & 1.993\% & 0.0987 & 1.89\% & 0.0961 & 0.795\% \\ HH-3.5-40\({}^{\circ}\) & 0.0872 & 0.08\% & 0.0858 & 1.681\% & 0.0853 & 2.25\% & 0.0961 & 10.12\% \\ HH-3.5-60\({}^{\circ}\) & 0.0654 & 0.05\% & 0.0657 & 0.406\% & 0.0648 & 0.97\% & 0.0655 & 0.101\% \\ HH-3.5-80\({}^{\circ}\) & 0.0476 & 0.05\% & 0.0475 & 0.158\% & 0.0484 & 1.73\% & 0.0476 & 0.052\% \\ SH-3.5-0\({}^{\circ}\) & 0.1695 & 0.05\% & 0.1698 & 0.128\% & 0.1374 & 18.98\% & 0.1696 & 0.010\% \\ SH-3.5-20\({}^{\circ}\) & 0.1043 & 0.10\% & 0.1042 & 0.0077\% & 0.1053 & 1.06\% & 0.0961 & 7.768\% \\ SH-3.5-40\({}^{\circ}\) & 0.0681 & 0.09\% & 0.0668 & 1.999\% & 0.068 & 0.24\% & 0.0681 & 0.092\% \\ SH-3.5-60\({}^{\circ}\) & 0.0767 & 0.12\% & 0.0778 & 1.551\% & 0.078 & 1.81\% & 0.0766 & 0.016\% \\ SH-3.5-80\({}^{\circ}\) & 0.1352 & 0.11\% & 0.135 & 0.254\% & 0.1109 & 18.06\% & 0.1353 & 0.032\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stiffness prediction errors (%E) between FEA and ML algorithms. other attainable motions and are a function of the lattice topology (and orientation) used. Here, this twist in non-symmetric lattices can be adjusted by setting the orientation of the lattices. This leads to the possibility of offering a more comprehensive range of options in terms of attainable movements in CJs. For example, a CJ that was initially intended to be a fully rotational joint can now allow more complex motions, similar to the ones encountered in universal or spherical joints. In addition, another way to control the motion range emerged from the mixing of different LFHs. This resulted in a new approach to manipulating the range of motion and the type of motion offered. Computational and laboratory experiments allow the characterization of motion range increments in additively manufactured CJs. However, variations in the experimental results are encountered. These are attributed mainly to the defects inherent in these manufacturing techniques. The use of support structures in FDM results in defects at the locations where these were joined to the manufactured part. In lattice structures, where the dimensions of the struts and cell walls are comparable to the dimensions of the bonding point between the supports and the part, these defects could more significantly affect the mechanical performance. ML approaches were demonstrated to be a successful alternative in mechanical engineering applications. In the case of the elastic displacement and stiffness of CJs, several algorithms were tested and compared with FE results. Of all the ML algorithms tested, GPR had the best learning results. This learning algorithm is nonparametric, i.e., not limited by a functional form, so rather than calculating the probability distribution of parameters of a specific function, GPR calculates the probability distribution of all admissible functions that fit the data, allowing admissible functions to more easily adapt to fit specific data even when the dataset is relatively small.