## 1 Introduction
The second approach is an indirect method using Laser Polymer Powder Bed Fusion (LP-PBF) technology to create the initial model, which is used, in turn, to create the mold used for the manufacturing using Injection Molding. Both approaches have limitations that require model processing before manufacturing. One example is the minimal element thickness imposed by both approaches. In the direct method, the limiting factor is the thermal stress, in the indirect method--the molten metal flow properties during the casting process. The use of parametric models in CAD software allows adjustments to the optimized model to consider those phenomena. Structural optimization is used as one of many steps in the Additive Manufacturing design workflow, which starts with product planning and ends at product validation [7]. The optimized part is further processed in CAD software to post-process it and prepare for manufacturing [8]. There are a lot of techniques for the post-processing of 2D structural optimization results, mostly based on image recognition techniques such as Hough transform [9], morphological processing [10] or features mapping [11; 12]. The selection of post-processing techniques in a 3D domain, due to the greatly increased number of topological complexities [13], is limited. Techniques are based on removing material from the initial full domain, by matching pre-defined 2D shape templates [14] or by matching machining features using dedicated algorithms [15] or machine learning [16; 17]. The skeletonization of structural optimization results enables tracing models with cubic B-splines, either as a manual, computer-aided process [18] or as a fully automated process [19]. Approaches gaining popularity with Additive Manufacturing [20] are based on mesh processing such as smoothing [21] without converting the results to a CAD model or converting the results to a CAD model as a watertight shell [22]. The goal of the used structural optimization algorithm is to equalize the surface strain energy density distribution. Topology with uniform distribution under a load has both maximal stiffness and minimal mass [6]. In nature, this phenomenon is observed in trabecular bones. Bone tissue is constantly remodeled as it reacts to the external stress by growing in areas with high stress concentration and removing where it is not required [23]. This bio-mechanic process is simulated in the structural optimization process to equalize surface strain energy density in the optimized model. The volumetric mesh generator integrated in the structural optimization system works using custom modification of the marching cubesalgorithm [24] optimized for use with FEM systems. The produced mesh is free of topology errors such as non-manifold surfaces [25] and is watertight [22]. This is the distinctive feature of an underlying approach compared to the Solid Isotropic Material with Penalization (SIMP) method [26; 27; 28]. The SIMP algorithm requires a dedicated discretization step with the desired mesh volume as a parameter. The biomimetic approach operates by modifying a mesh directly, starting from an existing design, which allows improving the existing designs [29] and uses the material mechanical properties to determine the maximal allowed stress. Hence, it is possible to use the output of structural optimization directly in a proposed feature recognition algorithm.