America Makes Awards $2M to Advance AI

America Makes and the National Center for Defense Manufacturing and Machining (NCDMM) have named the recipients of a project call funded by the Office of the Under Secretary of War for Research and Engineering, Manufacturing Technology Office (OSW ManTech), with a combined award of $2M.
The initiative, titled Artificial Intelligence for Material Allowables in Additive Manufacturing (AIM-4AM), focuses on a persistent bottleneck in additive manufacturing (AM) adoption: material qualification. Specifically, the project addresses how 17-4PH stainless steel (H1025), produced via laser powder bed fusion (LPBF), is currently tested and approved for use.
The qualification process for AM materials has traditionally relied on extensive physical testing — an approach that is both time-consuming and expensive. AIM-4AM aims to introduce machine learning (ML) to map process–structure–property relationships, identifying which tests yield the most meaningful data. The goal is to reduce redundant physical testing without sacrificing confidence in results, by assigning any reductions to defined, probabilistic risk categories.
What the Framework Aims to Deliver
If successful, the AI-driven framework would accelerate certification timelines and lower associated costs, while providing clearer, data-backed decision-making tools for production parts. Proponents say the approach could support faster deployment in both defense and civilian manufacturing contexts.
“AIM 4AM represents a critical step toward modernizing how we qualify and certify advanced materials, enabling faster, more data-driven decision making across defense and industrial applications,” said John Martin, AM Research Director at America Makes. “By applying artificial intelligence to target the highest value tests and quantify risk with greater precision, the Institute is helping to reduce uncertainty while accelerating the pathway to field-ready AM solutions.”
Award Recipients
A single team was selected to execute the project:
- Team Lead: Dyndrite
- Project Team: Mimo Technik Printed Metal, RTX Technology Research Center (RTRC)
The team will present progress reports at the America Makes Technical Review and Exchange (TRX) and other industry events throughout the program’s execution phase.
Closing the Gap Between Testing and Confidence
Previously, National Institute of Standards and Technology (NIST) criticized the lack of repeatable process outcomes in material qualification as an impediment to 3D printing’s widespread implementation. The difficulty is structural: without a reliable model linking how a part is built to how it performs, engineers must run tests to cover uncertainty rather than resolve it. AI tools are now being developed to address this directly — identifying relationships within process and experimental data to predict outcomes for material or process configurations that have not yet been physically tested, allowing teams to focus experimentation more effectively and reach results faster.
Senvol, working with NIST, applied ML to process–structure–property relationships with the goal of achieving faster qualification for 3D printed materials in line with traditional manufacturing timelines. America Makes had previously contracted Senvol’s ML software for aerospace material qualification in the defense sector—an effort that delivered what the company described as promising results.
These earlier programs established a consistent pattern — AI does not replace physical testing, but restructures how it is prioritized. AIM-4AM follows this trajectory with a stricter accountability requirement: any reduction in testing must be tied to quantified risk, making the trade-off between speed and confidence explicit.
3D Printing Industry is inviting speakers for its 2026 Additive Manufacturing Applications (AMA) series, covering Energy, Healthcare, Automotive and Mobility, Aerospace, Space and Defense, and Software. Each online event focuses on real production deployments, qualification, and supply chain integration. Practitioners interested in contributing can complete the call for speakers form here.
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Featured image shows America Makes and NCDMM logo. Image via America Makes.

Facts Only

America Makes and NCDMM received a $2 million award from OSW ManTech for the AIM-4AM project.
The project focuses on material qualification in additive manufacturing (AM).
The initiative uses machine learning (ML) to map process–structure–property relationships.
The project addresses the qualification process for 17-4PH stainless steel produced via laser powder bed fusion (LPBF).
The framework aims to reduce redundant physical testing.
Reductions in testing are tied to quantified, probabilistic risk categories.
The project seeks to accelerate certification timelines and lower costs.
A single team was selected: Dyndrite, Mimo Technik Printed Metal, and RTX Technology Research Center (RTRC).
Earlier programs demonstrated that AI restructures how physical testing is prioritized rather than replacing it.

Executive Summary

America Makes and the National Center for Defense Manufacturing and Machining (NCDMM) received a $2 million project award from the Office of the Under Secretary of War for Research and Engineering, Manufacturing Technology Office (OSW ManTech) for the Artificial Intelligence for Material Allowables in Additive Manufacturing (AIM-4AM) initiative. The project addresses the bottleneck of material qualification in additive manufacturing (AM). The goal is to use machine learning (ML) to map process–structure–property relationships, allowing the identification of the most meaningful tests and the reduction of redundant physical testing. This approach aims to accelerate certification timelines and lower costs while maintaining confidence in results by assigning reductions to defined, probabilistic risk categories. The work involves a team led by Dyndrite, including Mimo Technik Printed Metal and RTX Technology Research Center. The initiative follows earlier work, such as that by Senvol, which applied ML to process–structure–property relationships to achieve faster qualification for 3D printed materials.

Full Take

The shift toward an AI-driven framework that prioritizes testing based on quantified risk represents a fundamental restructuring of the burden of proof in engineering and manufacturing. The pattern observed is that the challenge in AM qualification is not merely technical complexity, but the structural inefficiency of relying on exhaustive physical testing to cover uncertainty. The progression from traditional, exhaustive testing to an AI system that targets high-value tests is not just an optimization of process; it is a mandate for accountability. By requiring that any reduction in physical testing be tied to explicit, probabilistic risk categories, the framework establishes a necessary bridge between technological speed and regulatory confidence. This challenges the assumption that speed inherently equates to safety or reliability, forcing the industry to internalize and quantify the trade-off between time and confidence. The implications suggest that future industrialization will rely on the successful establishment of these probabilistic risk models, moving the focus from brute-force verification to intelligent risk management across the entire manufacturing lifecycle.

Sentinel — Human

Confidence

The analysis is highly structured and fact-based, consistent with official project reporting or industry news, and shows no discernible signs of synthetic generation.

Signals Detected

Sentence length variance is natural, mixing technical specifics with broader explanatory goals.

The text successfully balances technical details with narrative framing, demonstrating a coherent voice.

Citations of specific entities (America Makes, NIST, specific teams) provide concrete anchors, indicating real-world sourcing.

The content follows a logical progression (problem -> solution -> history -> next steps) typical of well-structured press material.

Human Indicators

Specific names (John Martin, Dyndrite, Senvol) and organizational references (NIST, OSW ManTech) suggest direct reporting or official press material.

The discussion of the historical pattern (AI restructures testing, not replaces it) demonstrates nuanced synthesis rather than simple declarative claims.

The text includes promotional calls to action (AMA series, newsletter subscription) which is characteristic of published industry reports.