This solicitation closed on June 3, 2026
View current Department of Defense opportunities →Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
AI Overview
This SBIR seeks AI/ML and digital twin tools to optimize additive manufacturing parameters and predict mechanical performance across the entire component lifecycle. The solution addresses rapid production of legacy parts by integrating material selection, build optimization, and performance prediction into a single, system-agnostic platform for Navy manufacturing.
This summary is AI-generated from the official solicitation.
Key Details
Official 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 pa...
Change History
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Opportunity DON26BZ01-NV030 no longer available
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**Change Summary:** Only one modification identified: Q6 answer was updated with a timestamp notation "Response modified by the DON SBIR/STTR Programs on 2026 0601" clarifying that CMMC Level 2 Self Assessment is required by Phase I award with no inherent cost requirement. No new questions added; all other answers remain substantively identical.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Updated answers to Q1 and Q2 clarifying Phase I validation requirements: proposers may use existing datasets (NIST AM Bench) if they demonstrate how their AI/ML tool applies to that data; physical testing should use cast/forging-replacement components (not test coupons); focus is on mechanical performance prediction accuracy of final components, not material properties alone.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**Summary of Q&A Changes:** Added 2 new questions (Q1-Q2) clarifying Phase I validation scope: - Q1: Clarifies whether offerors can use existing published datasets (e.g., NIST AM Bench) instead of conducting physical testing, and whether single coupons or functional legacy-equivalent components are required. - Q2: Asks whether Navy seeks process/material-agnostic vs. specific framework, and whether new experimental data must be generated or existing datasets suffice for feasibility demonstration. Added Q3 requesting Navy confirmation that commercial AM facility subcontracting (rather than Navy-affiliated facilities) is acceptable for Phase I validation testing. All remaining Q&As (previously Q2-Q13) renumbered to Q4-Q15 with no answer changes. **Key Impact:** New questions directly address Phase I cost structure and testing requirements—whether contractors must generate new experimental data or can validate against existing public datasets.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**New Question Added:** Q1 addressing Phase I validation location - clarifies that offerors may conduct physical testing at commercial AM facilities under subcontract, rather than requiring Navy-affiliated facilities. **All Other Questions:** Renumbered (previous Q1-Q12 became Q2-Q13); no answer content was modified or updated.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**Updated Q1 Answer:** Clarified that while 2 different L-PBF manufacturers technically satisfy the Phase II requirement, Navy **prefers non-LPBF processes** (wire-arc DED, laser DED, powder-DED, etc.) for Phase II validation to demonstrate AI/ML tool versatility across different AM processes.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**Summary of Q&A Changes:** Added 1 new question (Q1) clarifying Phase II requirements: using 2 different L-PBF manufacturers satisfies the "different AM build systems" requirement for the 2 additional components/material classes validation.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**Only Q1 was updated with new answers.** The Navy clarified it has no specific material class or component type preferences—all AM materials suitable for replacing castings/forgings are acceptable. They also confirmed antenna/RF applications are out of scope. All other Q&As (Q2-Q11) remain unchanged.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**Changes to Q&A:** Added 1 new question (Q1) with 3 sub-parts addressing: material class priority (316L SS vs. alternatives), legacy component categories of acute Navy need, and whether RF/antenna components fall within solicitation scope (clarified as out of scope for this structural metals topic).
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**One new Q&A added:** Q1 clarifies that CMMC Level 2 Self Assessment (not full compliance) is required with no cost requirement beyond time, and must be met by Phase I award.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Status changed from Pre-Release to Open
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
# Q&A Changes Summary **Q1 - NEW ANSWER:** Added clarification that the AI/ML tool must ingest all widely available commercial CAD file formats. Compute capability will vary by deployment location; should not assume HPC availability. **Q2 - NEW ANSWER:** Clarified that access to slicing sample output is offeror's responsibility; Navy will not provide it. **Q3 - NEW ANSWER:** Refined assumption scope—slicing/print-plans handled by printer software for LBPF systems only, not necessarily for larger AM systems like wire-arc DED. All other answers (Q4-Q9) remain unchanged.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
**Q&A Changes Summary:** Added 3 new questions on technical implementation: file formats/compute resources (Q1), access to slicing software outputs (Q2), and printer software responsibilities (Q3). Provided new answers clarifying that the AI/ML tool must support all commercial metallic alloys and AM printers, predict material performance including strength/fatigue based on process parameters, and validate using traditionally-cast/forged components with no specific recommendations.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Added 5 new Q&As: Q1-Q5 address ML prediction scope (physical properties), number of materials/printers to support, process optimization focus, and Phase I component validation guidance. Q6 (previously Q1) retained with same answer on modeling approach flexibility.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
No changes detected. The Q&A content is identical between the previous and updated versions.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Q1 received answer: Government has no preference for specific AI/ML modeling approach; flexibility and customization across AM materials/processes is paramount to meet performance objectives.
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Q&A section updated
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Close Date changed from 2026-04-22 to 2026-06-03
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Open Date changed from 2026-03-25 to 2026-05-06
Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)
Status changed from Removed to Pre-Release
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