Multi-objective optimization of build orientation in additive manufacturing

Additive manufacturing is a production technology based on creating three-dimensional parts directly from computer-aided design data layer-by-layer. This technology provides a significant advantage in producing topologically optimized or lattices applied lightweight designs that cannot be produced with conventional manufacturing methods. In recent years, it has been used in many industries, especially in the aerospace and medical industry, with the production of functional, high-quality metallic parts with the powder bed fusion process by laser, which is one of the additive manufacturing technologies. The build orientation of the three-dimensional part has a major impact on many factors such as part quality, waste amount, production time, and cost. In this study, a multi-objective optimization is carried out using non-dominated sorting genetic algorithm-II to simultaneously optimize different objectives that may conflict with each other, such as the amount of support structure and build time. Estimation methods are developed for computing the amount of support structure and the build time, which reflect the current state of the technology. With the developed method, build orientation is optimized for a complex part, and the wide range of alternative results are visualized and evaluated. Three optimum alternative build orientations are selected depending on different weights of objectives by developed decision-making mechanism. The design for additive manufacturing knowledge required to perform the build orientation process correctly is eliminated by automating the pre-processing stage. Therefore, the contribution is made to the accessibility and sustainability of the powder bed fusion process by laser, which has high process costs by minimizing support structure volume and build time.