PhD Project by Christina König

Project Title: Microstructural characterization of additively manufactured (AM) graded metal components
Group: Nano-Micro-Macro. Stucture in Materials
Supervisor: Joerg Jinschek

Project
Metal additive manufacturing (AM) is distinguished by extremely high heating and cooling rates (approx. 10^3-10^6 K/s) that deviate significantly from equilibrium conditions, while subsequent layers are subjected to cyclic thermal exposures. These conditions lead to a unique "as-built" microstructure with distinctive properties. The field of functionally graded materials allows for the incorporation of desired gradients into the material during the manufacturing process, either through a locally varying chemical composition or sectionally varying microstructures. This enables specific localized properties to be tailored even in three-dimensional complex structures. To ensure reliability and robustness of a component, it is critical to comprehend the interdependence of the printing parameters, the materials composition and the complex final microstructure responsible for the resulting properties, e.g. strength.

AM components are typically several centimeters in size, while property-determining microstructural features (grains, phases and their orientation, pores, cracks,…) must be analyzed microscopically on a micrometer scale. Common research practice is to analyze only a small subset of the sample, resulting in vague predictions of the overall microstructure of a complex macro-scale AM component, disregarding the account of the localized process conditions described above.

In this project we will aim to develop a systematic analysis of the macro- /micro- / nanostructure to identify statistically significant variations in the microstructure as well as in mechanical properties. Further focus is on developing a fundamental understanding of underlying process-microstructure-property relationships. The ultimate goal is the development of a comprehensive database that can be used to train convolutional neural networks (CNN) to automate the microstructural analysis as well as the overall materials design process.

The project is funded by DTU’s “PhD grant for a joint Alliance Research Project”, in close collaboration with Prof. Peter Mayr (Chair of Materials Engineering of Additive Manufacturing) at the Technical University of Munich (TUM).