SBIR/STTR Research Award

PX 3D-AI Micro-NDI Tool

Award identifier ad390f7647b41f34a55fe75ddc925e3f0fdce1f3de76f60789521421b478b9e4Awarding agency Department of Defense · Navy

Award amount

$139,795

Program and phase

SBIR · Phase I

Award year

2021

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Award details

Awarding agency
Department of Defense
Branch
Navy
Recipient company
PHOTON-X, INC.
Proposal award date
2020-10-19
Contract end date
2021-04-19
UEI
RDJKJ3EBMPM7
Solicitation number
20.2
Topic code
N202-093

Research abstract

Photon-X’s goal is to solve the Navy’s Physical Measurement of Corrosion and Wear Damage on Splined Surfaces on V-22’s by utilizing innovative commercial 3D-AI NDI capabilities that identify and prioritize areas for repair and predict where future failures might arise. The company’s shape-based 3D-AI NDI technology platform has been developed over the past several years with a major international oil company, and Photon-X has a functional commercial NDI system. The base technology proposed for the V-22 is an integral part of an overall Photon-X Cognitive Autonomy NDI solution.  The system when used in conjunction with a borescope or other device that is capable of seeing the splines in the V-22 Proprotor Gearbox (PRGB), will quantify and qualify not only high-resolution surface corrosion, pitting damage, and metal loss but also provide 3D shape wear damage assessments and digital twin capabilities of the splines. This NDI data will allow DON to perform high-resolution inspections and allow for predictive maintenance.   The PX 3D-AI NDI Tool can is an integral part of the PX 3D-AI NDI Cognitive Autonomy NDI Platform and will provide passive, high-resolution, dynamic, real-time 3D sensing and analytics for advanced 3D surface inspections, predictive analysis, and digital twins, and the technology will serve as the base platform for additional functionalities covered desired by DON. The core of the product is Photon-X’s proprietary Spatial Phase Imaging Technology (SPI) sensors. The SPI sensors capture an additional EO dataset – angles/shape. With this added dataset and corresponding analytics, Photon-X’s 3D inspections have lab verified 10X improvement over traditional general inspection technologies and provide time-based to risk-based data capture. The NDI tool has achieved an accuracy of 0.00007” from a distance of 17feet in a ground truth test with a major aircraft manufacturer. Therefore, reaching the required accuracy of 0.0005” on a metallic surface are well within the parameters of the system.  The Photon-X Cognitive Autonomy NDI Platform as a whole offers inspection, maintenance, and change workflows that are more efficient and effective than current systems. As such, maintenance and data integration costs are lower, data can be more effectively updated and analyzed over time (change analysis, deep analysis for new insights, etc.), and safety is increased. Autonomous Inspections and simultaneous digital twins can now be a reality. With real-time analytics, digital twinning, and predictive analytics, Photon-X has a proven EO/AI Platform that can be modified and advanced to solve DON’s need for monitoring V-22 splines and other aircraft assets and the need for prioritizing aircraft maintenance.  The 3D-AI Cognitive Autonomy Platform can not only perform high-quality autonomous NDI and digital twinning but also find, track, locate, and uniquely identify multiple fast-moving objects and extract objects from their environments.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

30 SBIR/STTR records share this UEI (2001–2024). Reported award amounts total $10,463,359. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense, Department of Transportation, National Aeronautics and Space Administration

Agency intelligence

Department of Defense

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