Additive manufacturing (AM) of metals represents a suite of emerging technologies that manufactures three-dimensional objects directly from digital models through an additive process. AM enables the rapid production of complex parts with minimal lead time, fewer constraints, and reduced assembly requirements. This makes it an attractive option for fabricating customized, high value-added components in industries ranging from aerospace, automotive, and healthcare to defense.

Despite its transformative potential, widespread industrial adoption of AM faces significant technical challenges. The extreme processing conditions during the AM build process, including rapid heating and cooling rates exceeding 10^6 K/s, result in materials with residual stress, heterogeneous metastable microstructures, and nonequilibrium phases. These challenges make it difficult to establish the critical structure-process-performance relationships required for qualification and certification of AM components.

This NRC postdoctoral research opportunity builds on ongoing efforts at the Materials Measurement Science Division of the National Institute of Standards and Technology. The research seeks to address these challenges through an integrated approach combining high-speed X-ray diffraction (XRD) and other synchrotron-based scattering experiments and advanced data analysis. Central to this research is the development and application of real-time data analysis pipelines to process the vast, high-speed XRD datasets generated during AM processes. These pipelines will utilize advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data, with a focus on identifying critical transformation windows and assessing phase evolution kinetics.

The research activities include:

1. Designing and executing experiments using a Directed Energy Deposition testbed integrated with synchrotron XRD to monitor phase transformations and microstructural changes.
2. Developing advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data and phase transformation kinetics assessment.
3. Applying the insights gained to optimize AM processing parameters, explore novel processing conditions, and establish robust structure-process-property relationships for industrially important AM alloys.

This research is expected to be conducted through extensive internal and external collaborations, providing access to a full range of state-of-the-art materials characterization and computational modeling capabilities. The results will have broad implications for accelerating AM adoption across industries and advancing NIST's mission in measurement science.

You will primarily collaborate with a team consisting of Dr. Brian DeCost (brian.decost@nist.gov), Dr. Howie Joress (howie.joress@nist.gov), Dr. Austin McDannald (austin.mcdannald@nist.gov), and Dr. Fan Zhang (fan.zhang@nist.gov). We encourage you to reach out to any of us with questions about the position or our work.

key words

Additive Manufacturing; High-Speed X-ray Diffraction; Phase Transformation; Machine Learning; Structure-Process-Performance Relationships; Nonequilibrium; Metastable phases; Physics-Informed Algorithms; Real-Time Data Analysis

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants