Powder bed-based laser beam melting of metals enables the efficient production of complex components. The support structures usually required for the assembly of the 3D parts play a key role. A novel support model, inspired by bionic algorithms, promises significant material savings and high scrap security.
Inspired by nature: Bionic support structures for metallic 3D printing bind up to 83 percent less material.
(Image: iLAS_TUHH)
Susanne Bader, technical journalist for technology and industrial topics (automation, IT, Industry 4.0, materials, surface technology) in Munich/Germany.
When components are produced using selective laser melting (also known as PBF-LB/M technology), support structures play an important role. They support geometric overhangs and help to avoid internal stresses and distortion in the component by evenly dissipating heat.
However, the supports also have a negative impact on the cost of the production process, as their construction ties up materials and increases energy consumption. In addition, there are extended printing times and increased effort in post-processing. This additional time and financial burden poses major obstacles to small and medium-sized enterprises in particular. Therefore, it is important to reduce all these points in the interest of economic production.
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Software creates bionic and efficient support structures
In the project "Trees as Efficient Support Structures in Additive Manufacturing" (BEST), Cenit AG and the Institute for Laser and Systems Engineering (iLAS) of the Technical University of Hamburg worked together to develop a software tool for the uncomplicated creation of bionic and resource-efficient support structures for additive manufacturing processes. Branched branches of large tree crowns served as a model. The project was funded by the German Federal Ministry of Education and Research.
Support structures generated from top to bottom.
To determine the optimal shape and distribution of possible support structures, the cooperation partners compared different types of support structures and rated them based on the current state of research. Tree-like structures proved to be optimal supports in terms of fixing the components, thermal properties, and material consumption. To develop a 3D generation tool for creating these support structures, the BEST team first analyzed the known botanical algorithms L-Systems and Space Colonization.
A drawback of the L-system was the lack of control in the distribution of the connection points for the branches of the tree structure.
Space Colonization, on the other hand, sometimes generated branches that were too thick, resulting in additional costs.
Therefore, in the development of the software module, a simpler algorithm was used. This generates the supporting tree structures starting from the crown down to the trunk in the sense of a reversed growth.
How tree generation works and what advantages it offers
The BEST tree generation begins with the import of the Step file of the component. The software detects unsupported surfaces and edges. On these, it automatically creates contact points that transition into the trunk via a branch structure as an opposing support point. This creates a three-dimensional support structure in the form of a tree, which is visualized together with the component (Figure 1).
Figure 1: The most important parameters of the BEST tree structures.
(Image:iLAS TUHH)
A major advantage of the developed software is its high user-friendliness. The tree structures can be changed and adapted to individual requirements with just a few parameters, even without in-depth knowledge. The fact that the support structures are saved in the Step format is another plus of the tool. Since Step files are not tessellated, they can be manually edited with any CAD system. Moreover, the component model in Step format can also be used for the NC programming of post-processing. This is because additively manufactured parts often need to be machined afterwards, especially this applies to functional surfaces like bores, threads, contact surfaces, and fits.
BEST on the test stand.
To test the quality and suitability of the generated tree supports, the project team carried out an FEM analysis. The subject of this analysis were all steps of the manufacturing process. The primary goal was to compare the BEST support structure with the widely used block supports and the Netfabb-internal tree structures (Figure 2). The analysis focused on the maximum deformation, material consumption, and the percentage of failed elements, among other things. Finite elements in the simulation are considered failed when the yield strength of the material has been exceeded by the calculated loads.
Figure 2: A component supported by different support bodies.
(Image:Cenit AG)
In the test run, it was shown that the BEST structures far outpaced their competitors in terms of resource consumption: they bind up to 83 percent less material. The maximum deformations of all types of support structures were on a comparable level. Only the BEST supports showed no failed elements in comparison. However, the result of the widely used block supports with 10 percent failed elements should be validated again.
Advantages of biological principles in the PBF-LB/M process proven.
Figure 3: Demonstrator component for aviation with tree-like support bodies.
(Image:Cenit AG)
Overall, the BEST team was able to clearly demonstrate the advantages of biological principles in the PBF-LB/M process. At the end of the project, a large and complex demonstrator component from the aviation industry was used to test the developed module under extreme conditions and to highlight the full potential of the BEST development.
BEST component in post-processing of additive manufacturing.
(Image:Hein & Oetting Precision Engineering GmbH)
Building on this, an experimental validation of the simulated deformations was carried out by examining the additively manufactured demonstrator with a 3D scanner. The project team is now working to optimize the software tool and to implement additional functions, such as the integration of results from process simulations.
Date: 08.12.2025
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