The Department of Mechanical Engineering at Virginia Tech invites applications for a funded post-doctoral researcher position, starting as soon as possible.

Using metal additive manufacturing (AM) as a production process instead of a prototyping tool requires reliably and repeatably manufacturing parts with near-identical microstructure, surface topography, and properties. Consequently, understanding the uncertainty associated with the process-structure-property-surface (PSPS) relationship is of crucial importance. Yet, PSPS research is time-consuming and costly because many specimens are required to derive meaningful information and, alternatively, aggregating existing datasets of different studies and research groups to expand and enhance insights about the PSPS relationship is not straightforward because of access/permissions and inconsistencies between data formats.
This research project aims to combine an uncertainty quantification (UQ) framework with machine learning (ML) algorithms to derive data-driven models that relate laser powder bed fusion (LPBF) process parameters to metrics that quantify the microstructure and as-built surface topography. The knowledge resulting from this research will (1) quantify the uncertainty of the microstructure and as-built surface topography as a function of the L-PBF process parameters; (2) determine the LPBF process parameters required to obtain specific uncertainty (or probability definition) of microstructure and as-built surface topography; (3) derive an operating map of the solution of the forward and inverse problems and its uncertainty as a function of the L-PBF process parameters; (4) implement a cloud-based database to aggregate microstructure images and surface topography maps that can be cited using a digital object identifier (DOI).
The individual filling this position will be expected to work on characterizing the surface topography and microstructure of LPBF parts and relating that knowledge to LPBF process parameters through a UQ framework. Additionally, the individual will participate in STEM outreach at Virginia Tech.

The project is sponsored by the National Science Foundation, and is led by Prof. B. Raeymaekers and Prof. Pinar Acar in the Department of Mechanical Engineering at Virginia Tech. The postdoctoral associate will join two vibrant research groups (Raeymaekers and Acar groups) with PhD, MS, and undergraduate students, and they will have the opportunity to collaborate with other lab members and contribute to mentoring graduate students.
The project is currently funded, so the start date is as soon as practical. The position will be located at Virginia Tech in Blacksburg, VA. Candidates should submit a curriculum vitae, publication list, statement of research, and list of three references.