Additively manufactured structures have a wide range of applications, e.g. in mechanical engineering or biomedicine. Depending on the field of application, such metamaterials can be produced from high-quality metallic materials, plastics or biomaterials. Especially for large or very valuable printing processes, the required structural properties must be achieved in the first printing attempt („first time right“).

The aim of this work is to develop a concept for compensating for printing defects. By simulating the printing process while it is still in progress, the desired structural properties are ensured by replanning the rest of the printing process.

In addition, the student is given the opportunity to be familiarized with state-of-the-art machine learning technologies in Python in order to carry out dynamic process simulations accurately and effectively.