Additive Manufacturing of Metals

The Additive Manufacturing of Metals Project, which has participants from multiple groups and divisions, and has three primary objectives:

• Develop and utilize novel local and in situ measurement capabilities for establishing AM mechanisms responsible for processing and post-processing pathways in AM applications.
• Establish mechanisms leading to novel processing and post-processing pathways for AM applications while providing data and models to enable the development of optimized heat treatments and alloys.
• Provide curated world-leading measurement data and metadata for developing and improving simulation capabilities and standards that drive AM design and support qualification, certification, reliability, reproducibility, and digital twins.

Major Current Activities

Processing-structure-property-performance measurements and models

• Use coupled measurements and thermo-kinetic modeling to explore the composition dependence of AM-processed nickel Alloy 625 upon annealing time and temperature.
• Use coupled measurements and thermo-kinetic modeling to develop effective heat treatments for AM IN718 components in collaboration with the petroleum and natural gas industry. This work is part of a CRADA with Shell Oil Corporation.
• Use coupled high-speed measurements to explore the phase transformations of advanced AM aluminum alloys under build conditions.

Develop new in situ lab-based and synchrotron X-ray measurement capabilities for AM heat treatments and builds

• Use new in-house directed energy deposition AM platform and powder atomization system to explore the role of composition on AM-processed alloy behavior.
• Provide unique high-speed deformation and heating data on AM 17-4 SS to support AM research including hybrid manufacturing.
• Work with staff at the Advanced Photon Source (APS) to develop, install, and test a new generation of the existing ultra-small-angle X-ray scattering (USAXS) instrument, providing enhanced capabilities for measuring thicker samples, a larger range of precipitate sizes (sub-nm to micrometers), internal elastic strains, crystallographic texture, and phases in situ during thermal processing of metal specimens. This system is currently undergoing validation testing and conducting early measurements.
• Develop and deploy a new laser-powder DED testbed at the Advanced Photon Source for high-speed diffraction with real-time AI analysis for phase identification

Model validation

• Founded by the Additive Manufacturing of Metals project, the Additive Manufacturing Benchmark Series (AM Bench) is a NIST-led organization that provides a continuing series of AM benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark measurement data. AM Bench partners include researchers from 10 NIST divisions and 20 outside organizations. Complete AM Bench cycles were completed in 2018 and 2022, and work on AM Bench 2025 is in progress. For detailed information on AM Bench 2025, see www.nist.gov/ambench.   

  

Data management

• Work with NIST and external collaborators to develop and deploy comprehensive sample tracking and data management systems in support of AM Bench. Operational versions of all systems have been made public and serve as prototypes for broader data management within the Additive Manufacturing of Metals project and the Materials Science and Engineering Division.
  • AM Bench Website – Best source for information and data links concerning the AM Bench measurements, data, challenge problems, and conference series
  • NIST Public Data Repository (PDR) – primary access to all public AM Bench measurement data
  • Measurement catalog – searchable data and metadata curation system.
  • SciServer – Free analysis and processing of large AM Bench data sets can be conducted directly on the data server using your own or provided codes. This avoids the need to download TBs of measurement data and provides easy analysis using a Jupyter notebook environment.  AM Bench data on SciServer is copied from the NIST PDR.
• AM Bench GitHub – AM Bench users will be able to share models and codes that can run on the AM Bench SciServer or at your home institution - development in progress.

Additional outside collaborations

• Computational Materials for Qualification and Certification (CM4QC) is an industry/government agency/university steering group that is working to Identify key considerations and enablers required to increase the airworthiness certifying authorities’ and the industry’s acceptance of the use of computational methods for Q&C of structural AM parts. After nearly five years of development, the 192-page CM4QC Strategy Document is in the final stages of completion following reviews by 32 reviewers. Publication is expected Summer 2025. NIST is a founding member of CM4QC and serves on the CM4QC leadership team. Partners include:
  • Industry - Boeing, GE Aviation, Honeywell, Howmet Aerospace, Lockheed Martin, Northrop Grumman, Pratt & Whitney, Sikorsky, Spirit Aerosystems, Textron Aviation
  • Research Institutes - Southwest Research Institute
  • Government agencies - NIST, NASA Langley, Federal Aviation Administration, Air Force Research Laboratory, Army Aviation, Naval Air Systems Command, Oak Ridge National Laboratory, Sandia National Laboratory
  • Universities - Carnegie Mellon, Northwestern, Penn State, University of Texas San Antonio

Anticipated Outputs of Project

Measurements

• Develop and deploy new measurement capabilities to drive understanding of underlying processes
  • USAXS/SAXS/WAXS for in situ microstructure characterization during heat treatment (builds upon APS upgrade)
  • High-speed diffraction with real-time AI analysis for phase evolution

Mechanisms

• Provide clear understanding of microstructure evolution and performance of AM processed materials for our stakeholders

Data

• Promote development, dissemination, and adoption of technical standards for metal AM
  • API, ASTM, NIST Standards COE, ISO, TMS  
• Develop digital twin blueprint integrating physics-based models, UQ, data assimilation, and ensemble and surrogate models (collaboration with Microstructure Property Tools project)
• Deploy data systems & deliver measurement data/metadata to our stakeholders
  • AM Bench measurements, challenge problems, publications (journal, data, metadata)
  • Shell CRADA
• Enable broad incorporation of computational modeling into qualification & certification
  • Broad national impact, even beyond AM and aviation
  • AM Bench is a critical part of this