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PrintRite3D? AI/ML for In-Situ Additive Manufacturing Defect Detection

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0067
Agency Tracking Number: N222-117-0396
Amount: $239,995.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N222-117
Solicitation Number: 22.2
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-11-07
Award End Date (Contract End Date): 2024-04-08
Small Business Information
1712 Route 9 Suite 300
Clifton Park, NY 12065-3104
United States
DUNS: 010926207
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Matthew Brown
 (919) 869-8884
Business Contact
 Denise Hale
Phone: (518) 836-2178
Research Institution

Additive manufacturing (AM) increases the speed and flexibility of production and enables traditional part concatenation for advanced manufacturing capabilities. The ability for U.S. manufacturers to 3D-print advanced components in-house reduces reliance on traditional subtractive supply chains and bolsters national security readiness. While AM affords unique flexibility in design for manufacturability, its variance in lack of repeatability and reproducibility introduced various defects, including lack of fusion, gas entrapment, powder agglomeration, balling, internal cracks, and thermal stress, that degrades mechanical properties of final parts. The cost associated with performing post process inspection is an economic limiter and its efficacy is limited by material and material geometry, that is solved by in process nondestructive inspection methodologies. Kitware, in collaboration with Sigma Additive Solutions, proposes to bring the latest advances in deep neural network artificial intelligence and signal fusion to optimize and extend PrintRite3D® for the Navy’s unique needs. PrintRite3D is a platform-independent, interactive, in-process quality assurance system that combines inspection, feedback, data collection and critical analysis. Optimizing PrintRite3D defect detection accuracy will improve confidence in and reduce part-rejection false-alarm rates. Our proposed method builds on an existing proof of concept for in-situ defect detection and extends our capabilities to cover a wider range of builds, printers, locations, and sensors.

* Information listed above is at the time of submission. *

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