## 4 Discussion  
After the results are analyzed, in the present section, their relevance and implications are discussed; finally, the future research directions are indicated. The main novelty of this research lies in the fact that lattice structures can be simulated as solid blocks with fictitious material properties that imitate the thermal behavior of the lattice structure. Considering the lattice structure as a solid material reduces not only the computational time required for the FEM simulation but also the size of the generated data. The specific heat of the fictitious material is the same as for the lattice material, but the density and thermal conductivity need to be adapted accordingly. On the one hand, the density of the fictitious material is the same as that of the original material but is reduced according to the fill percentage. On the other hand, the conductivity reduction depends on both the fill percentage and the shape of the lattice structure. Therefore, for a certain fill percentage value, components with different thermal conductivities can be designed. Stretched lattices in the direction of heat transfer result in more efficient heat transfer. The limit case would be if the lattice were composed of columns in the direction of heat transfer. In this case, the effective thermal conductivity would match the fill percentage, whereas, in the perpendicular direction, the thermal conductivity would be zero. Theoretically, thanks to the lattice structures, the effective thermal conductivity of the structure could vary between zero and the fill fraction of the thermal conductivity of the solid block. Nevertheless, printing restrictions should be accounted for in order to avoid overhang areas that require support structures. Note that the limit for the use of support structures may depend on the employed machine's characteristics. Regarding the properties of the material employed to print the lattice structure, their values need to be defined in the FEM model. In the present research, the as-deposited Inconel 718 has been characterized, but, if the same material under different heat treatments or other materials are to be simulated, the FEM model would need to be fed with the corresponding material properties. However, the developed methodology would be still valid. Therefore, the lattice structure behaves as a thermally anisotropic material, in which the effective thermal conductivity can be adjusted without modifying the weight of the part. This offers design engineers a new parameter to optimize their designs. Consequently, the next step would be to define the directional thermal conductivity of the lattice structures as a function of the fill percentage and the shape of the lattice, which could be introduced in topological optimization software. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Lattice**} & \multicolumn{3}{c}{**Solid**} \\ \cline{2-7}
**Case** & \begin{tabular}{c} **Number of** \\ **Elements** \\ \end{tabular} & \begin{tabular}{c} **Elapsed Time** \\ \end{tabular} & \begin{tabular}{c} **MAPDL Result** \\ **File Size** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Elements** \\ \end{tabular} & \
\begin{tabular}{c} **Elapsed Time** \\ \end{tabular} &
\begin{tabular}{c} **MAPDL Result** \\ **File Size** \\ \end{tabular} \\ \hline
1 & 41,993 & 2 h 3 min & 89.698 GB & 2066 & 15 min 11 s & 4.47 GB \\
2 & 42,530 & 2 h 6 min & 92.439 GB & 2054 & 18 min 5 s & 3.80 GB \\
3 & 35,848 & 1 h 41 min & 88.872 GB & 3016 & 21 min 34 s & 5.27 GB \\
4 & 43,631 & 2 h 12 min & 112.02 GB & 2736 & 23 min 15 s & 7.16 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computational cost for the lattice and solid geometry simulations for the different cases. In addition, the capability of FEM tools in simulating the thermal behavior of lattice structures is beyond doubt.