Examples of TO and lattice structures

© C. Hölzl

Figure: Examples of TO and lattice structures

Supervisor: C. Hölzl

Co supervisor: J. Stampfl

PhD student: S. Geyer

Objectives: The goal of this PhD project is to develop algorithms for topologically optimized parts that can be produced by means of SLS and Hot Lithography. Using proven software tools such as SolidWorks for the design of models, Altair Inspire for topology optimization, ANSYS for verification via FEM and both Rhinoceros and Grasshopper for the development of algorithms for structural optimization via non-conformal lattice structures, an easy to use toolchain for part optimization is to be developed. In the scope of the development of algorithms, the potential of using machine learning algorithms will be evaluated and compared with conventional algorithms. For that purpose, components from the open source machine learning library LunchBoxML will be used and adopted.

Both mentioned Additive Manufacturing processes are to be used to produce the optimized parts that in a next step are to be verified via given tools of material testing. Parts produced using the Hot Lithography process additionally have to be optimized in respect of support structures so that no additional support is needed.

The key goal of this project is the development of tailored algorithms that automatically optimize input data from CAD and FEM software under the boundary conditions of design space, fixtures, loads and the soft kill option (SKO) approach, as well as physical parameters specific to the material/fabrication system used, to minimize weight and maximize stiffness of the resulting geometries.

FH Campus Wien will provide software needed to design and optimize part design as well as mentioned fabrication processes. Furthermore, FH Campus Wien will provide supervision for the development of algorithms and machine learning related topics.

TU Wien will provide tools and machinery to verify the optimized and manufactured parts.