Metals-based Additive Manufacturing Grant Program | 2020 NIST MBAMGP 01

Metals-based Additive Manufacturing Grant Program (NIST) FAQs

What is the Metals-based Additive Manufacturing Grant Program?

The Metals-based Additive Manufacturing Grant Program is a discretionary funding opportunity run by the National Institute of Standards and Technology (NIST). It supports measurement science and related research aimed at removing major obstacles to broad adoption of metals-based additive manufacturing (metal AM), with an emphasis on making metal AM more reliable, repeatable, and scalable for real-world production.

What is the main goal of this funding opportunity?

The main goal is to strengthen the measurement and standards foundation needed for consistent metal AM performance. The program prioritizes practical, technically grounded work that improves how metal AM is measured, characterized, monitored, controlled, and qualified.

What kinds of projects is NIST looking to fund?

NIST is looking for research that addresses core measurement and performance barriers in metal AM, including feedstock characterization, machine and process characterization, real-time process monitoring and control, process optimization and throughput improvements, and rapid qualification methodologies for processes and parts. The program also calls out model-based approaches and computational requirements for systems integration.

How does this program define "practical" research?

Based on the description, "practical" work is technically grounded research that directly improves measurement, characterization, monitoring, control, and qualification of metal AM processes and parts, with a clear line of sight to improving reliability, repeatability, and scalability for production settings.

What is meant by "measurement science" in this context?

In this opportunity, measurement science refers to research that improves the ability to measure, characterize, and link observable attributes (for example, powder properties, in-process sensor signals, surface quality, geometric accuracy, and material properties) to build outcomes and performance expectations in metal AM.

Does the program focus on metals-based additive manufacturing specifically?

Yes. The opportunity is explicitly focused on metals-based additive manufacturing (metal AM) and the measurement, monitoring, and qualification needs that support broader adoption of metal AM.

What are examples of feedstock characterization topics mentioned?

The notice highlights feedstock characterization such as understanding and measuring powder properties and how those properties affect build outcomes.

What does "machine and process characterization" refer to?

It refers to understanding how machine settings and the physical behavior of the process translate into part quality, and improving the ability to measure and characterize those relationships.

What is meant by "real-time process monitoring and control"?

This refers to sensing what is happening during a build (process monitoring) and using that information to stabilize or correct the process (process control), with the intent of improving consistency and reducing variability.

Is process optimization and throughput within scope?

Yes. The program emphasizes improving process optimization and throughput, including methods to increase build efficiency without sacrificing quality, as well as approaches that enable faster learning cycles and more confident parameter selection.

What is "rapid qualification" and why is it a priority?

Rapid qualification methodologies for both processes and parts are a key priority area. This includes research that enables quicker and more credible pathways to qualify parts by characterizing and linking measurable attributes (such as surface quality, geometric accuracy, and material properties) to performance expectations.

What types of measurable attributes are specifically mentioned for qualification work?

The opportunity specifically mentions surface quality, geometric accuracy, and material properties as examples of measurable attributes that could be linked to performance expectations as part of rapid qualification methodologies.

Are model-based approaches encouraged?

Yes. The program explicitly calls out model-based approaches and the computational requirements for systems integration, indicating interest in projects that connect experiments, measurements, and predictive models in ways that can be adopted across equipment, materials, and production environments.

What does "systems integration" mean here?

From the description, systems integration refers to connecting experiments, measurement methods, sensor data, and predictive models into usable approaches that work across different equipment, materials, and production environments, including the computing and data infrastructure needed to support that integration.

What are examples of outcomes model-based approaches might predict?

The notice gives examples such as predicting defect formation, microstructure evolution, dimensional outcomes, or mechanical behavior by translating sensor signals and process parameters into performance-relevant predictions.

Does the program mention anything about data or computing infrastructure?

Yes. It references the computational requirements for systems integration and notes interest in the computing and data infrastructure needed to make integrated modeling and data approaches usable.

What kind of funding mechanism is used for this opportunity?

Funding is provided through a cooperative agreement.

What does it mean that the award is a cooperative agreement?

A cooperative agreement typically means NIST expects an active partnership role during the project rather than a fully hands-off grant.

What is the funding opportunity identifier?

The funding opportunity is identified as "2020 NIST MBAMGP 01."

What is the CFDA number for this program?

The program is listed under CFDA 11.609.

What activity category is this opportunity listed under?

It is within the Science and Technology and other Research and Development activity category.

When was the notice created?

The notice was created on June 8, 2020.

What was the original closing date?

The original closing date was August 6, 2020.

Is the award ceiling provided in the source information?

No. The source information provided does not list an award ceiling.

Does the notice state how many awards are expected?

No. The source information provided does not list the number of expected awards.

Who is eligible to apply?

Eligibility is broadly open to non-Federal entities. This includes accredited institutions of higher education, nonprofit organizations, and for-profit organizations incorporated in the United States, as well as state, local, territorial, and Indian tribal governments. It also allows foreign public entities and foreign organizations to apply.

Are individuals eligible to apply?

No. The notice explicitly excludes individuals because they are not considered non-Federal entities under the terms of the announcement.

Are unincorporated sole proprietors eligible to apply?

No. The notice explicitly excludes unincorporated sole proprietors because they are not considered non-Federal entities under the terms of the announcement.

Can Federal entities apply for or receive funding under this program?

No. Federal entities cannot receive funding under this program.

Can Federal partners still participate in a project?

Yes. Federal entities are allowed to participate as unfunded collaborators, which can be useful for applicants who want to work with Federal laboratories or other Federal partners without routing award funds to them.

What does "unfunded collaborator" mean in this announcement?

In the context provided, it means a Federal entity may participate in the collaborative research plan but cannot receive funding through the award.

Does the program emphasize adoption across different environments and platforms?

Yes. The notice signals interest in approaches that can be adopted across equipment, materials, and production environments, particularly through integrated measurement, modeling, and data approaches.

Is the program aimed at improving repeatability and scalability for production?

Yes. A central theme of the opportunity is making metal AM processes more reliable, repeatable, and scalable for real-world production.