Developing novel restrictive design for additive manufacturing (DfAM) constraints for NURBS-based adjoint shape optimization for metal AM

The widespread application of metal additive manufacturing (AM) technologies has enabled exploration of complex design spaces to achieve optimally performing components. Current optimization techniques make use of several advanced methods, such as adjoint shape optimization, to provide designs that are superior to existing versions. However, they seldom discuss the manufacturability of the optimal designs. This research introduces novel restrictive design for AM (DfAM) constraints through computer-aided design (CAD) file modification which were used to guide the adjoint shape optimization process. The baseline design, using an application of a gas turbine fuel injector, was parameterized using non-uniform rational B-splines (NURBS) surface information stored in standard initial graphics exchange specification (IGES) file format. Gradient information computed using a commercial computational fluid dynamics (CFD) solver was used for NURBS shape modification in Python while focusing on imposing overhang angle and thin wall constraints for metal-AM. A method was developed to selectively replace information in the IGES file to accommodate modified design of surfaces of interest while preserving the overall geometry and maintain file integrity. The proposed framework accounts for varying levels of design complexity, accepting gradient information from commercial simulation software while imposing user-defined metal-AM constraints to obtain an optimal, additively manufacturable design. Findings from this study can be readily implemented in DfAM of any surface fluidic devices produced via metal laser AM, specifically Laser-Powder Bed Fusion.