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Innovative Solutions for Legacy Systems Modernization Using Plastic Additive Manufacturing

AFWERX · AFWERX TACFI · AFWERX

AI-Readiness Score
14/25
Pathway Speed
4/5
Timeline Realism
3/5
Problem Framing
3/5
AI / ML Fit
2/5
Award + Transition
2/5

Award

$1,164,133
Award ceiling
ELEVAN LLC
Awardee
Posted May 9, 2025

Description

Many Original Equipment Manufacturer (OEM) landing gear components, originally designed and fabricated in the late 1970s, lack detailed three-dimensional (3D) models and comprehensive material data, which limits their compatibility with modern Additive Manufacturing (AM) technologies. As a result, the 417th Supply Chain Management Squadron (SCMS) remains heavily reliant on conventional machine shops and legacy suppliers -- often incurring high costs, extended lead times, and inconsistent product availability. Building on the work to solve the landing gear challenges for the B-1, in this TACFI, Elevate Systems will analyze plastic weapon system components and will either convert flat 2D Data or reverse engineer assets for prototype 3D Printing. Elevate will also perform studies on the available polymer materials that are available today since the technologies are progressing so fast. This effort will leverage Elevate Systems’ developmental OPTIX™ (Optimized Technical Information Extraction) platform to comprehensively search existing technical data, drawings, FLIS data, and National Stock Numbers (NSNs) across the 448th Supply Chain Management Wing/Engineering Directorate. Our objective is to identify plastic components suitable for more cost-effective production utilizing organic (Air Force) and commercially available 3D printing.

Score Rationale

The TACFI pathway is a genuine fast-track instrument earning a high pathway score, but the core problem is additive manufacturing and supply chain logistics — not fundamentally AI-shaped work. The OPTIX platform mention suggests some AI/ML tooling for document/data extraction, but AI is clearly grafted onto what is essentially a reverse-engineering and 3D-printing materials study, pulling the AI/ML fit score down. The ~$1.16M ceiling with a named incumbent (Elevate Systems) and no explicit production transition pathway described limits the award/transition score to the low end.

Source

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