High-Speed X-ray Analysis for Metal-Based Additive Manufacturing
NIST is offering a postdoc to develop high-speed X-ray analysis and machine learning tools to improve metal additive manufacturing by understanding material changes during the process.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | May 1, 2025 | August 1, 2025 | November 1, 2025
Funding Amounts: $82,764 stipend plus $3,000 travel allowance; typical appointment duration 2 years.
Summary: Postdoctoral fellowship at NIST to develop high-speed X-ray diffraction and machine learning methods for advancing metal additive manufacturing by analyzing phase transformations and microstructural evolution in real time.
Key Information: Open to U.S. citizens with a doctoral degree; requires contacting research advisers prior to application; NIST participates in February and August review cycles.
Description
This postdoctoral research opportunity, offered through the NRC Research Associateship Programs at the National Institute of Standards and Technology (NIST), focuses on advancing metal-based additive manufacturing (AM) technologies. AM enables rapid fabrication of complex, customized metal parts with applications in aerospace, automotive, healthcare, and defense industries. However, industrial adoption is hindered by technical challenges arising from extreme processing conditions, such as rapid heating and cooling rates exceeding 10^6 K/s, which cause residual stresses, heterogeneous metastable microstructures, and nonequilibrium phases.
The research aims to overcome these challenges by integrating high-speed X-ray diffraction (XRD) and synchrotron-based scattering experiments with advanced data analysis techniques. Central to the project is the development of real-time data analysis pipelines that leverage machine learning and physics-informed algorithms to process large, high-speed XRD datasets. This approach will identify critical transformation windows and assess phase evolution kinetics during AM processes.
Key research activities include:
- Designing and conducting experiments using a Directed Energy Deposition testbed combined with synchrotron XRD to monitor phase transformations and microstructural changes in situ.
- Developing and applying advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data and evaluating phase transformation kinetics.
- Utilizing insights gained to optimize AM processing parameters, explore novel processing conditions, and establish robust structure-process-property relationships for industrially relevant AM alloys.
The project involves extensive collaboration within NIST and with external partners, providing access to state-of-the-art materials characterization and computational modeling resources. Outcomes are expected to accelerate AM adoption across industries and support NIST’s mission in measurement science.
Applicants will primarily collaborate with Dr. Brian DeCost, Dr. Howie Joress, Dr. Austin McDannald, and Dr. Fan Zhang.
