Review of state-of-the-art research on the design and manufacturing of support structures for powder-bed fusion additive manufacturing

Official URL: https://dx.doi.org/10.1007/s40964-023-00419-6

The use of support structures is an essential requirement for powder-bed fusion additive manufacturing (AM) processes. Supports are responsible for fixing the component on the build plate, carrying the weight of the structure, providing heat dissipation from the component to the build plate and preventing distortion during the process. Support efficiency and performance can be evaluated through the ease of removability, strength, thermal management, cost-effectiveness, and material consumption. As the support structures are the waste material during manufacturing of metal AM components, their design has a significant impact on the productivity and cost of the manufacturing process. Due to lack of concentrated information on the effect of each mentioned support function, this paper aims to gather studies and innovations in support design and production, specifically for the powder-bed fusion methods. At first, the effect of support type and contributing geometrical parameters on the overall performance of support structures is discussed. Then, an in-detail approach is taken to categorize each key characteristics of metallic support structures and reinforce the discussion with related published papers. Finally, the role of topology optimization (TO) in designing optimum support geometry is presented. The overall conclusion is that unless there are several studies on design and manufacturing of support structures, achieving the best setup has not been guaranteed by the existing tools. The research trend is toward developing more cost-effective optimization methods based on genetic algorithms (GA) and multi-objective functions to generate automated and high-performance supports, especially for complex geometries. Furthermore, integrating AM constraints with GA and TO can be achieved through defining self-supporting index or coupling with multi-objective optimization methods, which leads to a more efficient solution.