## 3 Results
### Case Study 2--Box Corner  
The second analyzed case study is a feature recognition run for the structural optimization of an aluminum box corner. The structural optimization has been performed as part of the mechanical design workflow. The manufacturing technique is 3D printing using the Powder Bed Fusion (PBF) technique. For manufacturing pre-processing, at the moment of this paper's creation, surface smoothing is considered. The following case study has been performed to evaluate the reconstruction of a parametric model as a pre-processing \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Input Mesh** & **Parametric Model** \\ \hline Total Mass & 18.41 kg & 18.35 kg \\ Max von Mises stress on surface & \(551\times 10^{6}\) Pa & \(436\times 10^{6}\) Pa \\ Mean von Mises stress on surface & \(55.7\times 10^{6}\) Pa & \(62.6\times 10^{6}\) Pa \\ Max Deflection & 2.008 mm & 1.340 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of mass and stress values for input mesh and parametric model for Case Study 1. Figure 16: Two selected fragments of the object reconstructed in CAD exhibiting excessive von Mises stress at joints—bending moments under load are not sufficiently handled. (**a**) Central node of the mesh. (**b**) Node with tension element (bottom) and two compression elements (left and right). Figure 15: Boxplot showing von Mises stress distribution on a surface, calculated for both the feature recognition input mesh and the output parametric model. Compared to the input mesh, the distribution of the von Mises stress on a surface of the parametric model is more concentrated, but the mean value is higher. step for use in this particular case. The structural optimization using aluminum (\(\
u=0.33\), \(E=70\times 10^{9}\) Pa, \(\rho=2800\) kg/m\({}^{3}\)) was performed with multiple (three) load scenarios. The maximum allowed von Mises stress was set to \(70\times 10^{6}\) Pa. The input geometries are shown in Figure 17. The boundary areas are color coded and used to describe the multiple load scenarios in Table 3. The optimization was run for 200 steps and the results of the optimization are shown in Figure 18. The prototype manufactured for the initial evaluation is in Figure 19.  
The mesh from the structural optimization was directly used as an input for the feature recognition system. Unlike in Case Study 1, this optimization problem contains areas excluded from optimization. Those areas were removed from the mesh in the pre-processing step. Refinement was run with the parameters displayed in Table 4. The output from the feature recognition process steps is shown in Figure 20. Structural analysis was performed after the parametric model was obtained, and areas excluded from optimization were reattached to the reconstructed object in a CAD system. The altered mesh was exported from the CAD system in an STL format and used for the structural analysis. As the optimization problem contains three different load scenarios, the structural analysis was also performed three times, once for each load scenario. Table 5 contains a summary of the analysis. Figure 21 shows the surface stress distribution for each load scenario, which are also presented as boxplots (Figure 22). Figure 17: Definition of the structural optimization system input shapes in the CAD system. (**a,c**) Two different views of the element to be optimized. (**b**) Optimization exclusion area and three boundary areas. (**d**) Optimization domain and three boundary areas. Colors of boundary areas are used in load cases descriptions. Figure 16:0 Photograph of one of the prototypes of the optimized box corner. This particular element has been processed with surface smoothing [21] and manufactured using FDM technology. Aluminum box edges are attached to the optimized object’s edges—areas excluded from optimization.