Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM) - SBIR Topic DON26BZ01-NV030

Disclaimer:
This topic was temporarily posted by the Department of War SBIR Program on March 2nd 2026 and removed the following day.
We believe this topic is planned to be released once the SBIR program is reauthorized; however, this topic may ultimately be modified or withdrawn.

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Funding Amount:

Est. $240,000

Deadline to Apply:

Est. April 29th, 2026.

Objective:

Automate additive manufacturing (AM) through advanced computational techniques (i.e., artificial intelligence and machine learning [AI/ML], digital twins, etc.) to select optimal materials and manufacturing parameters to meet mission requirements in terms of component performance.

Description:

AM has enabled new designs and rapid fabrication. However, there are no automatic tools available to computationally link across build platform to part performance. This SBIR topic seeks to leverage AI/ML, digital twins, and process simulation to select optimal materials and manufacturing parameters to meet rapidly changing mission requirements. A user should be able to input material type, part geometry, and AM system details into the prototype tools to automatically generate optimized build parameters along with accurate mechanical performance predictions.

While some tools in the current market can address part of this need, none are known which can integrate across the entire material lifecycle from pre-build to performance in a single ready-to-use package. The focus of this effort will be investigating legacy parts (i.e., obsolete castings and forgings) which need rapid production to avoid long lead times. Leveraging physics-informed AI/ML technologies and digital twins to optimize printing based on geometry and material properties will mitigate build defects and reduce post-processing while enabling performance prediction.

From a technical standpoint, the prototype tool(s) developed under this topic should seamlessly integrate across the component lifecycle, from initial design (or reverse engineering) to build parameter optimization to mechanical performance prediction in structural metals, to enable the user to accurately fabricate mission-critical components. The tool(s) must be part and AM build system agnostic to ensure scalability to multiple locations across the Navy’s manufacturing enterprise with various materials, systems, and performance requirements.

Who will win?

If you can achieve the objective above better than any other company on the market, you have a very high-likelihood of success and should apply.

Who is eligible to apply?

Any company that meets the following criteria:

• For-profit company

• U.S.-owned and controlled.

• 500 or fewer employees (including affiliates)

How Can BW&CO Help?

1) End-to-end support including, strategy, writing of the full proposal, and administrative & compliance support.

2) Proposal strategy and review.

3) Administrative & compliance support.

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