Additive manufacturing (AM) has revolutionized the production of aerodynamic components by enabling the rapid prototyping of complex geometries. However, the reliability of these components remains an active area of research. This study investigates: How do varying 3D printer parameters affect the reliability of additively manufactured aerospace components? Three objectives were defined: first, to identify optimal printing temperatures; second, to determine optimal thickness and velocity for the 3D printer settings; and third, to integrate the results using global optimization to determine the ideal set of parameters. Nozzle and bed temperatures were varied independently, and the results were compared using microscopic analysis. External perimeter speed and layer thickness were also tested independently to determine optimal settings. Systematic variations were applied to temperature, external perimeter speed, and layer thickness, following a statistical approach. Wing test sections were 3D printed to test the reliability of varying AM parameters. Microscopic analysis was then conducted to examine the reliability of the external surface of each print, capturing variations due to the different parameters. Finally, a machine learning algorithm and a global optimization were performed in order to determine the ideal set of AM printing parameters. This work bridged the gap between the reliability of AM and its applications in aerodynamics.
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