Responsibilities and tasks
The project is part of a new interdisciplinary and highly collaborative research group headed by Prof. Joerg Jinschek (DTU Nanolab). It 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).
Tasks (in close collaboration with the group at TU Munich):
- Develop a basic understanding of materials engineering for additive manufacturing;
- Develop reliable, robust procedures for the characterization of AM graded metal components – at the micrometer and nanometer length scale;
- Determine structural phenomena (and their statistical relevance); as well as the link to specific properties (e.g. mechanical, magnetic);
- Develop a concept to understand process-structure-property relationships;
- Provide data sets to train convolutional neural networks (CNN) to automate materials design processes.
Characteristics:
- You are interested in working in interdisciplinary research collaboration;
- You are a team player and have a strong talent for interacting in teams and integrating information;
- You have a fundamental interest in materials, materials processing-structure-properties, materials characterization;
- You want to learn about additive manufacturing (AM) technology;
- You want to learn and apply materials characterization;
- You want to learn how artificial intelligence concepts revolutionize materials science and engineering.
In order to design metal AM components with controlled properties and reliable performance, we need to develop a deep understanding of the link between AM alloy compositions & process parameters, and resulting local microstructure & properties. AM components (multi-material or graded structures) will be fabricated by either laser metal deposition, plasma metal deposition, or wire arc additive manufacturing (at TUM). This on one hand allows producing load-optimized parts, but on the other hand challenges our current metallurgical understanding. Collaboration partners at TUM will investigate the printing strategies, collect sensing and control data of the printing process for further analysis.
The PhD Scholar at DTU (this position) will perform a methodical analysis of the macro- / micro- / nanostructure utilizing various materials characterization techniques available at DTU, including X-rays and electron probes. Through a systematic evaluation, statistically significant variations in the microstructure as well as in microhardness and/or magnetic properties will be revealed to identify trends. You will further focus 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.
Qualifications
You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. A master degree in Materials Science, Mechanical Engineering, or Physics is preferred.
Experiences in materials characterization/diagnostics (especially electron microscopy) as well as with metals and processing are beneficial.
Good communication & writing skills as well as a very good command in English is expected.
Further information
Further information may be obtained from Professor Joerg Jinschek, e-mail: jojin@dtu.dk
You can read more about DTU Nanolab at www.nanolab.dtu.dk