The US Department of Commerce’s National Institute of Standards and Technology (NIST) has awarded nearly $4 million in grants to help accelerate the adoption of new measurement methods and standards to advance US competitiveness in metals-based additive manufacturing (AM).
Additive manufacturing typically creates parts and components by building them layer by layer, based on a 3D computer model that is virtually sliced into many thin layers. Metals-based additive processes form parts by melting or sintering material in powder form. The process offers advantages such as reduced material waste, lower energy intensity, reduced time to market and just-in-time production.
Through its own research and with these grants, NIST is addressing barriers to adoption of additive manufacturing, including surface finish and quality issues, dimensional accuracy, fabrication speed, material properties and computational requirements.
The following organizations will receive NIST Metals-Based Additive Manufacturing Grants Program funding to be spent over two years:
Georgia Tech Research Corporation ($1 million). This project will analyze detailed data gathered during a powder bed fusion process to both control the manufacturing and predict the final properties of the manufactured parts. The goal is to establish a comprehensive basis to qualify, verify and validate parts produced by this technique. The initial focus will be on an alloy of titanium that could see extensive applications in the health care and aerospace sectors.
University of Texas at El Paso ($1 million). This project will define a test artifact that will standardize the collection of data on the process inputs and performance of parts made via laser powder bed fusion, an important method of metals-based AM. Academic, government and industrial partners will replicate the artifact and collect data on the key inputs to the process and the resulting properties of the artifact for a data repository. The work will lead to a greater understanding of the AM process and will allow for greater confidence in final parts.
Purdue University ($999,929). Qualification of parts made by AM now requires an extensive set of tests. This project aims to reduce that burden by developing a standardized approach to predict key performance properties through measurements of material microstructures and the use of mathematical models. The work promises to create a streamlined method for industry to understand part performance with less testing than is currently required.
Northeastern University ($999,464). This project aims to improve sensing approaches and create a suite of sensor technologies that will help optimize cold spray additive manufacturing. Cold spray AM processes have the potential to create parts that are more durable and stronger than those made with other AM processes. New sensors will help characterize the properties of the powder feedstock and the key parameters of the process, such as temperatures and part dimensions, and allow for better control of this promising technique.
In addition to these awards, NIST anticipates funding additional projects as part of a second phase of awards in the first half of 2021.