3D printing is becoming ubiquitous in engineering and science. One of the main reasons for such success is its ability to create small structures not producible in any other known way. Typical examples include lightweight but strong materials (resembling e.g. honeycombs) and artificial tissue. Such materials need to have specific material properties, while the production is subject to uncertainties appearing in the printing process. This project grows out of the need for mathematical algorithms to find optimal structures that retain their outstanding properties even in the presence of small errors. For lightweight materials that are used to build lighter cars, planes and rockets that save fuel, robustness is key. Similarly, 3D-printed artificial tissue has to mimic the real human tissue of fire-victims to a high degree. The proposed methodology reduces the cost of an optimization-based product design cycle by orders of magnitude compared to the most efficient existing approaches.

2023

2027