## 4 Discussion  
Although there are multiple structural optimization methodologies, the challenge of a transition from a finite element mesh to parameterized model is always the same. Depending on the selected manufacturing method, it is possible to limit the optimization results processing to surface smoothing and continue the design process by relying on the manufacturing method preparation steps. The separation of the design optimization step and the manufacturing preparation step simplifies the overall manufacturing workflow but in practice is often insufficient--it is impossible to backtrack from the latter step and introduce adjustments other than direct operations on a mesh. The approach presented in the paper, coupled with the structural optimization process, allows for an interpretation procedure that uses a mesh generated by the discretization process, tailored for the finite elements method. Moreover, using a developed mesh generator, it is possible to adapt a feature recognition system to interpret results from any structural optimization system by remeshing the results. Extending the current implementation to allow for such operations is planned in the future. The conversion of the structural optimization results back into parametric models is an important research area that is vital not only for the preparation of the manufacturing process, but also when the mechanical design process requires, after the structural optimization step, more design cycles based on parametric models in CAD systems. The presented solution meets this demand while providing another feature desired for industrialization--a fully automated process. In the context of the mechanical design process, the proposed solution shifts the paradigm of the structural optimization step. The structural optimization step no longer breaks the consistency of the overall design process by transforming the solution from the parametric form to a finite element mesh. The structural optimization coupled with automated feature recognition transforms a parametric model into an optimized parametric model. From the viewpoint of engineers involved in industrial design, the whole process is consistent and uses parameterized models that can be easily modified or adjusted end-to-end. The described process is just a framework enabling further development. The main areas of improvement are as follows:  
* Truss-based geometry was chosen because, compared to more complex geometries, it has fewer degrees of freedom to optimize, so the heuristic refinement process converges fast. The chosen structural elements are insufficient for structures with bending moments under load and there is a need for support of more complex geometries to properly capture the complexity of the input meshes. The proposed heuristic re  
Figure 24: Mesh clipped to show stress concentration at Load Case 2 conditions at connection point between structural elements. (**a**) Structural optimization output. (**b**) Reconstructed parametric model. Flattened bar that carries bending moments, in structural optimization results it bears distinctive resemblance to the I-beam, which is designed for this purpose. In the parametric model, due to limitations, it was approximated with an element that is not suited for such loads. finement step and the Fitness Function in particular work with solutions built out of any parameterized geometries. This makes it possible to apply the algorithm to any geometries produced by structural optimization algorithms, without the current limitation to trusses. * The refinement process is heuristic; hence, the final solution may not meet the required mechanical properties. There is a need for research into the viability of adding an additional final refinement step where feedback from structural analysis is used to adjust the model. * Heuristic refinement in this proof of concept work utilizes a random search approach. This process can be highly optimized. For example, adjusting positions of solution graph nodes is possible with the use of gradient descent methods.