DON27TZ02-NV013 TITLE: Computational Frameworks for the Development and Qualification of Additive Manufacturing-Tailored Wire Feedstock for Naval Directed Energy Deposition

COMPONENT TECHNOLOGY PRIORITY AREA(S): Advanced Computing and Software;Advanced Materials;Sustainment

PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)

OBJECTIVE: Develop an integrated computational and experimental framework that engineers, qualifies, and specifies advanced metal wire feedstock specifically tailored for wire-fed Directed Energy Deposition (DED). By leveraging Integrated Computational Materials Engineering (ICME) and Super Intelligence/Machine Learning (SI/ML), this capability will enable high-confidence, point-of-need manufacturing of structural naval components by resolving the fundamental feedstock-process mismatch that currently limits DED mechanical performance.

DESCRIPTION: 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 Super Intelligence/Machine Learning (SI/ML) surrogate models that consume upstream feedstock history to predict how a specific wire lot will respond to DED thermal conditions and should employ SI-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.

PHASE I: Demonstrate the scientific and technical feasibility of the proposed ICME and SI/ML framework for one primary naval alloy system.

Develop initial multi-scale computational models linking upstream wire manufacturing variables to critical feedstock properties (including strict diametric tolerances, surface topography, and thermomechanical state) and AM deposit behavior, with the specific goal of predicting and mitigating a known and documented performance deficit in the selected alloy. (Note: Models shall be validated against existing data or targeted experiments.)

Deliver a comprehensive Phase II plan with a clear go/no-go criterion against a defined predictive accuracy target for the novel material/alloy being investigated.

PHASE II: Conduct a computationally guided experimental campaign to manufacture tailored feedstock lots that meet predicted specifications. Perform AM builds to demonstrate quantified property improvements against established baseline performance data for current commercial materials.

Leverage ICME and SI/ML-guided qualification paths to establish B-basis property allowables with a statistically significant reduction in physical testing.

Provide required Phase II deliverables to include: (1) A report on how the proposed novel alloy meets or exceeds current commercial offerings; (2) Details on how ICME and SI/ML techniques were leveraged to accelerate alloy development and scale-up; and (3) A prototype software tool embodying the integrated framework. Prepare a Phase III commercialization/transition plan.

PHASE III DUAL USE APPLICATIONS: Deliver the final prototype alloy to U.S. Navy facilities ready for part-scale verification and validation along with the previously completed testing results to demonstrate the feasibility of the proposed alloy. Follow-on demonstrations to non-Navy participants will enable other DoW, DoE, government, and industry partners to ability to view the solution and continue transition to other facilities.

REFERENCES:

1. Allison, J.E. et al. "Integrated Computational Materials Engineering: a New Paradigm for the Global Materials Profession.": JOM 58(11), pp. 25-27, 2006/2011). https://link.springer.com/article/10.1007/s11837-011-0053-y
2. Panchal, J.H. et al. "Key Computational Modeling Issues in ICME." JOM 65(2), 2013, pp. 231-245. DOI: 10.1007/s11837-012-0512-3
3. DebRoy, T. et al. "Additive Manufacturing of Metallic Components." Prog. Mater. Sci. 92, 2018, pp. 112-224. DOI: 10.1016/j.pmatsci.2017.10.001
4. Ding, D. et al. "Wire-Feed Additive Manufacturing of Metal Components." Int. J. Adv. Manuf. Technol. 81(1-4), 2015, pp. 465-481. DOI: 10.1007/s00170-015-7077-3
5. The Minerals, Metals and Materials Society (TMS). "Implementing ICME in the Aerospace, Automotive, and Maritime Industries." TMS, 2013. https://www.tms.org/portal/portal/Publications/Studies/ICME_Implementation_Study.aspx
6. ASTM International. "ASTM F3187-16(2023): Standard Guide for Directed Energy Deposition of Metals." ASTM International, 2023. https://store.astm.org/f3187-16.html

KEYWORDS: Directed Energy Deposition; DED; Wire-fed Additive Manufacturing; Laser Wire DED; Alloy-Process Co-Optimization; ICME; Physics-Informed Machine Learning; Bayesian Active Learning; B-Basis Qualification; Computational Materials Passport; Thermomechanical P

TPOC 1: Mike Brindza
michael.r.brindza.civ@us.navy.mil

TPOC 2: Charles Fisher
charles.r.fisher73.civ@us.navy.mil