Artificially Intelligent Object with Virtual Presentation of Engineering and Logistics Data
Navy SBIR 2019.2 - Topic N192-065
NAVAIR - Ms. Donna Attick -
Opens: May 31, 2019 - Closes: July 1, 2019 (8:00 PM ET)


TITLE: Artificially Intelligent Object with Virtual Presentation of Engineering and Logistics Data


TECHNOLOGY AREA(S): Air Platform, Battlespace, Information Systems ACQUISITION PROGRAM: PMA275 V-22 Osprey

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.


OBJECTIVE: Develop a web-enabled object and application that encapsulates three major areas of Technical Data (TD) into an all-in-one TD Virtual Reality (VR) structure able to quickly exhibit different views within a viewer, based on role and responsibility; incorporated with Artificial Intelligence (AI) to capture and make predictive maintenance analysis, detect and address anomalies, and provide a complete traceability of maintenance and part history. An AI auto update of all related TD, as the design is improved, with the ability to identify errors within Concurrent Engineering Logistics Layered Structure (CELLS) is desired. Develop and demonstrate a knowledge hub for capturing and distributing the maintenance predictions and TD updates. Communication must be both visual and verbal.


DESCRIPTION: Navy TD are currently stored in several database management systems, both in digital and paper formats, primarily in government systems: JEDMICS (Joint Engineering Data Management Information and Control System) and TMAPS (Technical Manual Application System). Numerous proprietary PLM (Product Lifecycle

Management) systems are also utilized by engineers and contain Computer Aided Design (CAD) systems. Three areas, all separate products, need to be incorporated into CELLS: Engineering models (a digital representation of the engineering design with sufficient metadata to manufacture the end item, system, component, and or part) [Refs 6, 7, 8, 9]; Interactive Electronic Technical Manual Systems (IETMS) (the maintenance instructions and supply details associated with the end item, system, component, and or part) [Refs 2, 3, 4, 5] presently NSIV (NAVAIR Standard IETMS Viewer); and NATOPs (Naval Air Training and Operating Procedures Standardization) (the operator instructions for Navy aviation pilots [Ref 1]). Currently the Navy pays multiple times for the same data in multiple formats. This proposed system would eliminate that and empower our logisticians to be able to view the needed data in real time, lowering cost and increasing efficiency.


The Navy needs a single system capable of providing multiple views of the design and logistics data, based on the audience in a consolidated engineering and logistics VR/AI object. The CELLS object must contain accurate measurements/geometry, scalable representations for different displays, and contain all associated metadata, normally included in IETMS and engineering models. It should have role-based viewing capability to include Engineering, Maintenance, Supply, Manufacturing, Production, Weapons System Operator, Tech Data Managers, and Foreign Military Sales (FMS) customers and other users such as analysts, testers, and quality assurance professionals. A Natural Language Processor (NLP) should be considered for language conversion.


Through AI technologies, the system should have the ability to capture information gained during usage and feed it to a Knowledge Hub within the Naval Air Technical Data and Engineering Service Center (NATEC) website. The Knowledge Hub, to be developed as a part of this effort, should then provide new knowledge updates as needed.


The intent is for a web-enabled capability to be housed as an application on the NATEC website. The CELLS application should be able to be utilized and downloaded based on the client’s role and responsibilities on a Navy Marine Corps Internet (NMCI) or similar level of compliance approved computer. CELLS should be designed for compatibility with both the enterprise (NATEC website) and client NMCI users as well as hardened laptop clients, and compliance with cybersecurity and must meet system DoD accreditation and certification requirements [Refs 10, 11]. The SBIR topic includes development of Application Programing Interface (API) documentation for potential use by developers of other DoD systems to interface with CELLS. A downloadable application for portable clients (e.g., on tablets) from the NATEC website as well as direct live from the website use is preferred. The proposer should utilize the MIL-STD-1388 (obsolete but still used by commercial industry often via software programs such as Raytheon’s Eagle Logistics Support Analysis Record) [Ref 12] as a basis and related Technical Data and engineering government standards as well as commercial standards utilized by the Government for Engineering Models such as but not limited to Citrix (Viewer for Interactive Electronic Technical Manual System), RDP (Remote Desktop Protocol), and Product Lifecycle Management (PLM) systems used by the Naval Aviation Enterprise.


NATEC web hosting environment: OS (Operating System): Windows Server 2012 R2, Web Server: IIS 7.5, Database: MS SQL Server 2014, .NET Framework 4.5, API: Simple Object Access Protocol (SOAP), eXtensible Markup Language (XML) Browsers: Edge/Chrome/Firefox, Security: HBSS compatible, no mobile code deployment (no ActiveX, no Java Applets, and no run-time callouts to 3rd party code libraries. All software and software libraries must be vendor supported and Functional Area Management (FAM) approved (or able to be certified for FAM approval).

Client machine environment: OS: Windows 10, RAM: 6GB, Processor: i5, HD: 250Gb, Graphics: Intel HD 4600 (1Gb RAM)/NVIDIA Quadro (1Gb RAM), Browsers: Internet Explorer/Edge/Chrome/Firefox, Security: HBSS compatible, no mobile code deployment (no ActiveX, no Java Applets), no run-time callouts to 3rd party code libraries. All software and software libraries must be vendor supported and FAM approved (or able to be certified for FAM approval)


Proprietary standards used by the various CAD software developers will factor into this as well, presently, HTML5 3D model data viewers.


Although not mandatory, development of an automated conversion process to turn the legacy TD into CELLS would be optimal. If an automated conversion capability utilizing AI and other technologies, it is estimated that CELLS could be created within five days or less. If done manually with current, ordinary technologies, it is estimated the conversion process could take 1-2 years.

The CELLS viewer must be web-enabled Virtual Reality Modeling Language (VRML) 3D+. Printing to 3D+ Portable Data File (PDF) is required. Virtual Reality depictions of TD in CELLS should be viewable from a display and not require any wearable technology. The resulting system should be available and capable to run 24/7.


User training should be designed and built into the online system.


CELLS directly VRML or alternatives such as 3DMLW, COLLADA, O3D, U3D, X3D, and/or WebG should be used and based on the best quality of the depictions. The proposer should use those languages typically used for developing AI; Python, C++, Java, LISP, and Prolog. Computer Aided Software Engineering (CASE) tools are encouraged if deemed useful by the developer. The proposer should utilize API's programmed to support standard SOAP, an XML-based messaging protocol for exchanging information among computers. The AI developer’s design consideration should be: Cognitive learning, Neural Network, NLP, Fuzzy Language, and more, to be incorporated as the vendor deems logical in the operations and capabilities of CELLS.


Note: NAVAIR will provide selected Phase I performers with the appropriate guidance for human research protocols so they have the information to use while preparing their Initial Phase II proposals. Institutional Review Board (IRB) determination as well as processing, submission, and review of all paperwork required for human subject use can be a lengthy process. As such, no human research will be allowed until Phase II and work will not be authorized until approval has been obtained, typically as an option to be exercised during Phase II.


PHASE I: Design and develop a concept for a system in accordance with the requirements in the Description. Determine the feasibility of the proposed system using the V-22 aircraft as the basis for the Phase II prototype. Consider the intricacies that are required for CELLS to be able to capture all the intelligence (Engineering Models, IETMS, and NATOPS) that are pertinent to Navy aircraft. Include high-level graphic depictions of potential prototypes of CELLS for components and/or systems that are in at least one of each: avionics, airframe, landing gear, hydraulics, and engine. Develop a draft CELLS Master Plan and draft specification to be the basis for Phase II. Include high-level prototype plans to be developed under Phase II. (Note: The Navy will only prototype with ‘Unclassified components and/or structures’.


Note: Please refer to the statement included in the Description above regarding human research protocol for Phase II.


PHASE II: Develop a prototype system. Test with CELLS to include all related products as provided in the description, to include both Alpha and Beta testing phases. In addition, test for the reconnection and updating of data and processes both to and from the Knowledge Hub and within CELLS. Any design changes should automatically update the incorporated areas of affected Technical Data, as well as any other areas of technical and sharing of predictive maintenance. Some of the other areas are to the Product Lifecycle Management Systems (PLMS) for specific programs. Continue executing and updating Master Plan. Once a prototype system has been developed, perform user testing with fleet maintenance and supply personnel and update prototype with any improvements and recommendations captured. Develop and build in training for both the functional clients and the technologists.

Develop draft process for use within NAVAIR enterprise and technical architectural flows. Continue to develop the draft specification.


Note: Please refer to the statement included in the Description above regarding human research protocol for Phase II.


PHASE III DUAL USE APPLICATIONS: Perform testing of the built-in training developed for the functional clients. Provide in-person training for technologists and also build into CELLS and incorporate into the CELLS standard. Support the transition of the system and continued development of enhanced CELLS capabilities arising from the new requirements. Transition to appropriate users such as Defense Information Systems Agency (DISA) or the Navy.


Successful development will result in the ability to convert engineering models, IETMS, NATOPs into CELLS: Viewer, Knowledge Hub, Maintenance Predictability, Auto Update and knowledge capture and distribute capability,

and process flows. The CELLS Standard could also be used by industry for direct support of Navy, but also for industry’s own internal usage in design, manufacturing, maintenance, and customer support. AI and other technology tools for automated conversion process would be game changing to interested owners of legacy TD that want to quickly convert their data into CELLS. An all-in-one solution would be available to industry capable of saving both time and money. This has the potential for becoming an international specification. Industries that could potentially benefit from this developed technology would be in manufacturing and aerospace. It would provide advantages for fast-moving design updates in the automobile industry.



1.   “MIL-DTL-80525B(AS) MIL-DTL-85025B, Department of Defense Detail Specification: NATOPS Program Technical Publication and Products; Style, Format, and Common Technical Content (28 SEP 2007).


2.  IASD S1000D issue 3.0, download available from


3.  IASD S1000D issue 4.1, download available from


4.   “MIL-STD-3001 Rev A Change 1 Dec 2016. MIL-STD-3001/8A (W/ CHANGE-1), Department of Defense Standard Practice: Illustrated Parts Breakdown (IPB) (Part 8 of 8 Parts) (01-DEC-2016).”


5.   “MIL-DTL-81310G, Detail Specification: Manuals, Technical: Airborne Weapons/Stores Loading/Weapons Assembly/Support Equipment Configuration (31 MAR 2008),” MIL-DTL/MIL-DTL-81310G_13972/


6.   “Engineering Drawing Practices.” ASME Y14.100, - 2017. Sheet.pdf


7.   “MIL-STD-31000, Department of Defense Standard Practice: Technical Data Packages (TDP) (05 NOV 2009) [SUPERSEDING MIL-DTL-31000C].” 31000_20516/


8.   “ISO 10303 - 1 - 1994 - Industrial Automation Systems and Integration - Product Data Representation and Exchange - Part 1: Overview and Fundamental Principles First Edition.”


9.   “ISO 14739-1 - 2014 - Document management - 3D use of Product Representation Compact (PRC) format - Part 1: PRC 10001.”


10.   “DoDI 8510.01, Risk Management Framework (RMF) for DoD Information Technology (IT).”


11.   “DoDI 8500.01, Cybersecurity.”


12.   “MIL-STD-1388/2B, Military Standard: DOD Requirements for a Logistic Support Analysis Record (28 MAR 1991) [S/S BY MIL-PRF-49506].” 2B_21788/


13.   DoDI 8510.01, Risk Management Framework (RMF) for DoD Information Technology (IT), dated 12 March 2014.

14.   DoDI 8500.01, Cybersecurity, dated 14 March 2014.


15.   DoDI 8582.01, Security of Unclassified DoD Information on Non-DoD Information Systems, dated June 6, 2012, Change 1, October 27, 2017.


KEYWORDS: Concurrent Engineering Logistics Layered Structure; CELLS; Artificial Intelligence, Virtual Reality Viewer; Predictive Maintenance; Virtual Technical D



Lawrence Herbert





Shane McDarby





Scott Moore





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