Computational Frameworks for the Development and Qualification of Additive Manufacturing-Tailored Wire Feedstock for Naval Directed Energy Deposition

STTR · Phase: BOTH · Topic DON27TZ02-NV013 · Solicitation 27.TZ

As the Department of the Navy prepares for contested logistics environments, the ability to manufacture large, mission-critical replacement parts at the point of need has become a strategic imperative. Wire-fed DED is uniquely suited to this role: it operates safely with reactive metal alloys, achieves high deposition rates for large structural volumes, and is robust enough for shipboard and expeditionary deployment. Despite this operational advantage, a persistent and quantifiable performance gap prevents wire DED parts from meeting Navy structural qualification standards without extensive post-processing. The root cause of this gap is a thermal physics and geometric mismatch. Additive manufacturing (AM) processes span approximately five orders of magnitude in solidification cooling rate, which is the master variable governing grain size, phase selection, and precipitate morphology. Current wire feedstock is manufactured to welding wire specifications, engineered for arc welding thermal histories rather than the specific precision and cooling rates of robotic DED. The consequences are well-documented: • Microstructural Deficits: Precipitation-strengthened aluminum alloys exhibit a 40-70% yield strength reduction in DED compared to laser powder bed fusion, driven by insufficient cooling rates to nucleate strengthening dispersoids. Titanium alloys (e.g., Ti-6Al-4V) develop continuous grain boundary alpha films that degrade ductility and fatigue life. • Residual Stress Instability: Residual stresses retained from the wire drawing process cause wire tip instability (cast/helix) in the DED melt zone, producing porosity and build failures. • Geometric and Surface Topography: Macroscopic inconsistencies in wire diameter dictate volumetric feed-rate errors, causing fluctuating energy-to-mass ratios in the melt pool. Simultaneously, surface roughness (poor smoothness) induces micro-stuttering in the robotic feed conduit and scatters laser impingement, severely destabilizing thermal absorption. No existing ICME framework addresses these deficiencies upstream of the melt pool—every published AM modelling chain begins at deposition, leaving the wire manufacturing sub-chain entirely unmodelled. The Navy seeks a comprehensive end-to-end computational framework that closes this gap through alloy-process co-optimization. Proposers should develop a model chain that links the entire wire feedstock lifecycle—from primary alloy synthesis and thermomechanical processing through final drawing and surface conditioning—directly to AM deposition dynamics and deposit properties. This model must account for the coupled effects of advanced state variables (e.g., dislocation density, oxide thickness). The framework should incorporate Artificial Intelligence/Machine Learning (AI/ML) surrogate models that consume upstream feedstock history to predict how a specific wire lot will respond to DED thermal conditions and should employ AI-guided experimental design to identify the minimum number of physical tests required to achieve robust statistical qualification. The ultimate deliverable is a computational materials passport: a traceable digital record linking a wire lot's manufacturing history to its predicted and validated AM performance, supporting the Navy's Digital Engineering Strategy and enabling condition-based qualification for fleet sustainment.

SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

Primary country

🇺🇸 United States

Also eligible

None

Foreign companies

No

Local entity

Required

Location condition

미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

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Official URL

https://www.dodsbirsttr.mil/topics-app/

Application URL

https://www.dodsbirsttr.mil/topics-app/

Last fetched

2026.11.06

Human review

Not reviewed — check the official announcement

First seen

2026.11.06

Data status

Auto-collected, not reviewed

AI enrichment

Needs verification