Artificial Intelligence and Machine Learning (AI/ML) for Additive Manufacturing (AM)

PROJECTED CMMC LEVEL REQUIREMENT
Level 2 (Self)
TECHNOLOGY AREAS
None
MODERNIZATION PRIORITIES
Advanced Computing and Software
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Advanced Materials
|
Sustainment & Logistics
KEYWORDS
Additive Manufacturing; AM; Artificial Intelligence; AI; Machine Learning; ML; AI/ML; Digital Twin
OBJECTIVE
Automate additive manufacturing (AM) through advanced computational techniques (i.e., artificial intelligence and machine learning [AI/ML], digital twins, etc.) to select optimal materials and manufacturing parameters to meet mission requirements in terms of component performance.
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 parameters along with accurate mechanical performance predictions.
While some tools in the current market can address part of this need, none are known which can integrate across the entire material lifecycle from pre-build to performance in a single ready-to-use package. The focus of this effort will be investigating legacy parts (i.e., obsolete castings and forgings) which need rapid production to avoid long lead times. Leveraging physics-informed AI/ML technologies and digital twins to optimize printing based on geometry and material properties will mitigate build defects and reduce post-processing while enabling performance prediction.
From a technical standpoint, the prototype tool(s) developed under this topic should seamlessly integrate across the component lifecycle, from initial design (or reverse engineering) to build parameter optimization to mechanical performance prediction in structural metals, to enable the user to accurately fabricate mission-critical components. The tool(s) must be part and AM build system agnostic to ensure scalability to multiple locations across the Navy's manufacturing enterprise with various materials, systems, and performance requirements.
PHASE I
Define and develop a concept which leverages AI/ML, digital twins, and process simulation to select optimal materials and manufacturing parameters to meet rapidly changing mission requirements. Perform modeling and simulation with pointed physical testing for validation on a single component to demonstrate feasibility of the proposed concept. Required Phase I deliverables (in addition to the Contract Deliverables listed in the DON BAA instruction) will include a report on how the proposed concept will be expanded should the proposer be awarded a Phase II contract.
PHASE II
Expand the concept into full prototype tool development and validation using at least two additional components of different material classes and AM build systems. Demonstrate reduction in material fabrication time through automatic parameter generation while also reducing defect rates and material waste. Required Phase II deliverables will include:
a) A report on how the proposed concept can be expanded to other materials and systems not demonstrated in the Phase I and II taskings
b) Production of prototype tool(s) ready for delivery and demonstration at two U.S. Navy affiliated facilities.
PHASE III DUAL USE APPLICATIONS
Delivery of the final prototype tool(s) to U.S. Navy facilities will demonstrate the feasibility of the proposed solutions. 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. The expectation is that the tool(s) will be leveraged by any organization in need of efficient digital tools to predict component performance based on manufacturing details.
REFERENCES
Beaman, J. J.; Bourell, D. L.; Seepersad, C. C. and Kovar, D. "Additive Manufacturing Review: Early Past to Current Practice." ASME.J. Manuf. Sci. Eng. November 2020, 142(11): 110812. https://doi.org/10.1115/1.4048193
Parvanda, R. and Kala, P. "Trends, opportunities, and challenges in the integration of the additive manufacturing with Industry 4.0." Prog Addit Manuf 8, 2023, pp. 587-614. https://doi.org/10.1007/s40964-022-00351-1
"FY2024 - Submarine Industrial Base: Program Year in Review." BlueForge Alliance/Submarine Industrial Base, November 1, 2024. https://www.buildsubmarines.com/newsroom/fy24-submarine-industrial-base-program-year-in-review