Additive Manufacturing (AM) is the process of joining materials by selectively depositing them layer upon layer for the purpose of manufacturing parts or assemblies which are based on a 3D digital model. The nature of these processes results in the morphing of complex component geometries, enabling a high degree of design freedom and resulting in lightweight structures with increased performance. These processes, however, experience many limitations regarding manufacturability. The aim of this study is to develop a method and tool that optimizes the design of a component to avoid overhanging geometries and the need for supports during the Additive Manufacturing process. A workflow consisting of steps for topology optimization, orientation optimization, material addition, and machine code generation is described and implemented using Rhinoceros 3D and Grasshopper software. The proposed workflow is compared to a conventional workflow regarding manufacturing Key Performance Indicators (KPIs) such as part volume, support volume, and build time. A significant reduction is observed regarding all the KPIs by using the proposed method. Examining the results from both the conventional workflow and the proposed one, it is clear that the latter has unquestionable advantages in terms of effectiveness. In the particular case study presented, a total volume reduction of around 80% is observed. The reduction in the total volume (including the required support volume) leads to a significant reduction in the material used as well as in the build time, consequently resulting in cost reduction.

Layout design is the process in which industrial robots and other manufacturing components are positioned within a manufacturing system so that the intended operations can be handled appropriately. The traditional layout design process presents several challenges. It involves numerous iterations of testing different manually generated manufacturing layouts, requiring extensive trial and error to achieve an optimal solution. This process is highly time-consuming and demands significant expertise and cognitive effort from the designer. Within this publication, a flexible, scalable, and efficient function-block-based solution is presented for the optimization of manufacturing system layouts, especially in the field of multi-robot cells in two different use cases: one in additive manufacturing and one in jig-less welding. The findings showcase that the methodology followed enabled the efficient allocation of industrial robots in a workspace, minimizing the cognitive effort required in comparison to the traditional manual trial-and-error layout design procedure.