# Topology Optimization of an Aerospace Bracket: Numerical and Experimental Investigation
## 4 Conclusions  
The integration of topology optimization into additive manufacturing for a practical bracket-design case has been successfully demonstrated with the analysis results experimentally validated under compressive loading. Two of the most common methods in topology optimization, particularly the level-set and density-based methods, were compared numerically and experimentally, and it was found that the level-set method showed more consistent results with respect to the analysis-predicted compliance levels, and a better match to the initial design's performance. Additionally, a study related to the appropriate identification of the regions selected for optimization was conducted, as the default method selections may not necessarily yield the desired results. Based on the results obtained, it was demonstrated that by selecting the appropriate discretization and optimization regions, we can produce consistent results with a 20% weight/material reduction while maintaining the stiffness performance of the initial design. Overall, the optimized components compete favorably with the original model, with a negligible difference in the mean deformation, especially for the level-set method.  
Figure 16: Comparison of the regions of failure with the corresponding regions of high-stress concentration in the analysis of the density-based optimized bracket (case 4). (**a**,**c**) depict the estimated stress distribution with (**b**,**d**) depicting the failure locations on the tested components; red arrows indicate the correspondence between high-stress and failure regions.  
Figure 15: Comparison of the regions of failure with the corresponding regions of high-stress concentration in the analysis of the level-set optimized bracket (case 4). (**a**,**c**) depict the estimated stress distribution with (**b**,**d**) depicting the failure locations on the tested components; red arrows indicate the correspondence between high-stress and failure regions.  
As indicated in this study, the selection of regions for topology optimization greatly affects the achieved designs, and future studies need to be carried out to improve the ability of the topology optimization models to automatically identify the optimization regions, which would result in better and more consistent results.  
Conceptualization, K.K. and O.O.; methodology, K.K. and O.O.; software, O.O.; validation, O.O., A.P., D.T. and K.K.; formal analysis, O.O. and K.K.; investigation, O.O.; resources, A.P. and D.T.; writing--original draft preparation, O.O.; writing--review and editing, K.K., A.P. and D.T.; visualization, O.O.; supervision, K.K.; project administration, D.T. and K.K.; funding acquisition, K.K. and D.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Nazarbayev University, Kazakhstan, under the Faculty Development Competitive Research Grants Program 2022-2024: \"Shape Optimization of Free-form Functional surfaces using isogeometric Analysis and Physics-Informed Surrogate Models--SOFFA-PHYS\", Grant Award Nr. 11022021 FD2927. Additionally, funding was received under the target program No. OR07665556 for the project entitled \"Additive Manufacturing Systems and Metal Powders for the Kazakhstan industry\", funded by the Ministry of Industry and Infrastructure Development of the Republic of Kazakhstan.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.