Optimizing Additive Manufacturing: Hints from Mother Nature
CENIT and Hamburg Technical University study trees as models for efficient support structures in 3D printing
The biologization of technology is becoming an increasingly important topic in materials and production research. Thanks to outstanding fundamental research, Germany holds a prominent position among countries that are devoting themselves to this field of study. The Federal Ministry of Education and Research is specifically promoting the transfer of insights from such research to sustainable industrial uses.
One of these efforts is the BEST project, in which experts from CENIT and Hamburg TU are examining support structures in additive manufacturing. As it turns out, they can take inspiration from mother nature’s blueprints and methods.
Optimization pressures in generating support structures for 3D printing
In additive manufacturing involving powder-bed laser beam fusion of metals (PBF-LB/M), complex geometries can only be generated if the right support structures are present. Currently available support structures are not ideal for the purpose. In one-off and small-batch production of components, this leads either to oversizing of the support structures or to printing errors. Additionally, excessive material consumption raises costs and prolongs printing times. Particularly for small and medium enterprises, this can present problems.
Algorithmic botany promises benefits for additive manufacturing
The BEST project aims to resolve these issues by developing support structures modeled on the natural growth of trees. They are highly resource-efficient without any sacrifices in terms of functional reliability. “Our studies have shown that the optimal structures have a tree-like geometry”, explains Jochen Michael, Senior Consultant at CENIT, in describing the approach.
The algorithm we have developed lets the tree-shaped structure grow inversely, from the crown down to the trunk
The support structures CENIT is generating in its project work are based on 3D simulations computed by experts from Hamburg TU. The combination of simulation, generative design and algorithmic botany has produced a computer-based tool that is able to generate tree-shaped support structures for additive component manufacturing.
Due to the levels of inherent tension that occur in additive processing, the project is focusing on titanium components made from the alloy Ti-6Al-4V. Here the challenge is the high melting point of the material (above 1600°C), which can deform the components. Therefore, the support structures have a threefold responsibility: they must dissipate heat evenly, absorb excess tensions and support geometric overhangs – a complex problem set that the BEST project tackles by applying the principle of inverse growth. “The algorithm we have developed lets the tree-shaped structure grow inversely, from the crown down to the trunk”, is how Jochen Michael describes the tool’s approach.
Insights for business and research
This tool for generating biologically inspired support structures is one of the key outputs of the project: It permits the development of optimized support concepts and boosts resource efficiency in additive manufacturing while simultaneously reducing material and energy consumption. These insights will benefit future CENIT client projects and will be integrated into future versions of FASTSUITE Edition 2, CENIT’s 3D simulation platform for the digital factory.
More information on the BEST Project
In the context of an idea competition entitled “Biologizing Technology”, the Federal Ministry of Education and Research is promoting research, development and innovation projects that have promising implications for materials or production research. One of these initiatives is the project “Trees as Efficient Support Structures in Additive Manufacturing (BEST)” (FKZ: 02P20E240), conducted by CENIT AG in collaboration with the Hamburg Technical University Institute for Laser and Plant System Technology. Project work began in Q3/2022 and is expected to be completed in Q2/2023.