DEPARTMENT OF THE NAVY (DON)17.B Small Business Technology Transfer (STTR)

Proposal Submission Instructions

INTRODUCTIONResponsibility for the implementation, administration, and management of the Department of the Navy (DON) SBIR/STTR Program is with the Office of Naval Research (ONR). If you have questions of a general nature regarding the DON's STTR Program, contact Mr. Steve Sullivan (steven.sullivan@navy.mil). For program and administrative questions, please contact the Program Manager listed in Table 1; do not contact them for technical questions. For technical questions about a topic, you may contact the Topic Authors listed for each topic during the period 21 April 2017 through 22 May 2017.  Beginning 23 May 2017 the SBIR/STTR Interactive Technical Information System (SITIS) (https://sbir.defensebusiness.org/) listed in Section 4.15.d of the DoD SBIR/STTR Program Announcement must be used for any technical inquiry. For inquiries or problems with electronic submission, contact the DoD SBIR/STTR Help Desk at 1-800-348-0787 (Monday through Friday, 9:00 a.m. to 6:00 p.m. ET). 

TABLE 1: DON SYSTEMS COMMAND (SYSCOM) STTR PROGRAM MANAGERSTopic Numbers Point of Contact Activity Email

N17B-T031 to N17B-T035 Ms. Donna Attick

Naval Air Systems Command(NAVAIR)

donna.moore@navy.mil

 The DON SBIR/STTR Program is a mission oriented program that integrates the needs and requirements of the DON’s Fleet through R&D topics that have dual-use potential, but primarily address the needs of the DON. Firms are encouraged to address the manufacturing needs of the defense sector in their proposals. More information on the program can be found on the DON SBIR/STTR website at www.navysbir.com. Additional information pertaining to the DON’s mission can be obtained from the DON website at www.navy.mil. PHASE I GUIDELINESFollow the instructions in the DoD SBIR/STTR Program Announcement at https://sbir.defensebusiness.org/ for program requirements and proposal submission guidelines. Please keep in mind that Phase I should address the feasibility of a solution to the topic. It is highly recommended that proposers follow the NEW DON proposal template located at www.navysbir.com/submission.htm as a guide for structuring proposals. Inclusion of cost estimates for travel to the sponsoring SYSCOM’s facility for one day of meetings is recommended for all proposals.

PHASE I PROPOSAL SUBMISSION REQUIREMENTSThe following MUST BE MET or the proposal will be deemed noncompliant and will be REJECTED.

Technical Volume. Technical Volume shall meet the following requirements:o Not exceed 20 pages; files exceeding 20 pages, regardless of page content, will be

REJECTEDo Single column format, single-spaced typed lineso Standard 8 ½” x 11” papero One-inch marginso No type smaller than 10-point

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o No imbedded tables, figures, images or graphics smaller than 10-pointo No letters smaller than a single pageo Data Rights Assertions, if required, should be provided in the table format required by

DFARS 252.227-7013(e)(3) and be included in the 20 page Technical Volume limito Include, within the 20 page limit, an Option that furthers the effort and will bridge the

funding gap between the end of Phase I and the start of Phase II. Tasks for both the Base and the Option shall be clearly identified.

NOTE:Phase I Options are typically exercised upon selection of the Phase II. Option tasks should be those necessary for movement from the Phase I feasibility effort into the Phase II prototype effort.

Cost. The Phase I Base amount shall not exceed $125,000 and the Phase I Option amount shall not exceed $100,000. Costs for the Base and Option should be separate and identified on the Proposal Cover Sheet and in the Cost Volume.

Period of Performance. The Phase I Base Period of Performance shall not exceed seven (7) months and the Phase I Option Period of Performance shall not exceed six (6) months.

DON STTR PHASE I PROPOSAL SUBMISSION CHECKLIST Proposal Template. It is highly recommended that proposers follow the NEW DON proposal

template located at www.navysbir.com/submission.htm.

Subcontractor, Material, and Travel Cost Detail. In the Cost Volume, firms shall provide sufficient detail for subcontractor, material and travel costs. Use the “Explanatory Material Field” in the online DoD Cost Volume for this information. Subcontractor costs must be detailed to the same level as the prime. Material costs shall include a listing of items and cost per item. Travel costs shall include the purpose of the trip, number of trips, location, length of trip, and number of personnel. When a proposal is selected for award, be prepared to submit further documentation to the SYSCOM Contracting Officer to substantiate costs (e.g., an explanation of cost estimates for equipment, materials, and consultants or subcontractors).

Performance Benchmarks. Firms must meet the two benchmark requirements for progress towards Commercialization as determined by the Small Business Administration (SBA) on June 1 each year. Please note that the DON applies performance benchmarks at time of proposal submission, not at time of contract award.

Discretionary Technical Assistance (DTA). If DTA is proposed, the information required to support DTA (as specified in the DTA section below) must be added in the “Explanatory Material Field” of the online DoD Cost Volume. Failure to add the required information in the online DoD Cost Volume will result in the denial of DTA. If proposing DTA, a combined total of up to $5,000 may be added to the Base or Option period.

DISCRETIONARY TECHNICAL ASSISTANCE (DTA)The STTR Policy Directive section 9(b) allows the DON to provide DTA to its awardees to assist in minimizing the technical risks associated with STTR projects and commercializing into products and processes. Firms may request, in their Phase I and Phase II Cost Volume, to contract these services themselves in an amount not to exceed the values specified below. This amount is in addition to the award amount for the Phase I or Phase II project.

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Approval of direct funding for DTA will be evaluated by the DON SBIR/STTR Program office. A detailed request for DTA shall include:

A DTA provider (firm name) A DTA provider point of contact, email address, and phone number An explanation of why the DTA provider is uniquely qualified to provide the service Tasks the DTA provider will perform Total provider cost, number of hours, and labor rates (average/blended rate is acceptable)

DTA shall NOT: Be subject to any profit or fee by the requesting firm Propose a provider that is the requesting firm Propose a provider that is an affiliate of the requesting firm Propose a provider that is an investor of the requesting firm Propose a provider that is a subcontractor or consultant of the requesting firm otherwise required

as part of the paid portion of the research effort (e.g., research partner, consultant, tester, or administrative service provider).

DTA shall be included in the Cost Volume as follows: Phase I: The value of the DTA request shall be included on the DTA line in the online DoD Cost

Volume worksheet. The detailed request for DTA (as specified above) shall be included in the “Explanatory Material Field” section of the online DoD Cost Volume worksheet and be specifically identified as “Discretionary Technical Assistance”.

Phase II: The value of the DTA request shall be included on the DTA line in the Navy’s Phase II Cost Volume (provided by the Navy SYSCOM). The detailed request for DTA (as specified above) shall be included as a note in the Cost Volume and be specifically identified as “Discretionary Technical Assistance”.

DTA may be proposed in the Base and/or Option periods. Proposed values for DTA shall NOT exceed: Phase I: A total of $5,000 Phase II: A total of $5,000 per 12-month period of performance

If a firm requests and is awarded DTA in a Phase II contract, it will be eliminated from participating in the DON SBIR/STTR Transition Program (STP), the DON Forum for SBIR/STTR Transition (FST), and any other assistance the DON provides directly to awardees.

All Phase II awardees not receiving funds for DTA in their award must attend a one-day DON STP meeting during the first or second year of the Phase II contract. This meeting is typically held in the summer in the Washington, DC area. STP information can be obtained at: http://www.navysbir.com/Transition.htm. Phase II awardees will be contacted separately regarding this program. It is recommended that Phase II cost estimates include travel to Washington, DC for this event.

EVALUATION AND SELECTIONThe DON will evaluate and select Phase I and Phase II proposals using the evaluation criteria in Sections 6.0 and 8.0 of the DoD SBIR/STTR Program Announcement respectively, with technical merit being most important, followed by qualifications of key personnel and commercialization potential of equal importance. Due to limited funding, the DON reserves the right to limit awards under any topic and only proposals considered to be of superior quality will be funded.

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Approximately one week after Phase I solicitation closing, e-mail notifications that proposals have been received and processed for evaluation will be sent. Consequently, e-mail addresses on the proposal coversheets must be correct.

Requests for a debrief must be made within 15 calendar days of select/non-select notification via email directly to the cognizant Contracting Officer provided in the select/non-select notification. Please note the DON debrief request period is shorter than the DoD debrief request period specified in section 4.10 of the DoD SBIR/STTR Program Announcement.

Protests of Phase I and II selections and awards shall be directed to the cognizant Contracting Officer for the DON Topic Number, or by filing with the Government Accountability Office (GAO). Contact information for Contracting Officers may be obtained from the DON SYSCOM Program Managers listed in Table 1. If the protest is filed with the GAO, please refer to the instructions provided in section 4.11 of the DoD SBIR/STTR Program Announcement.

CONTRACT DELIVERABLESContract deliverables are typically progress reports and final reports. Deliverables required by the contract shall be uploaded to https://www.navysbirprogram.com/navydeliverables/.

AWARD AND FUNDING LIMITATIONSThe DON typically awards a Firm Fixed Price (FFP) contract or a small purchase agreement for Phase I. In accordance with STTR Policy Directive section 4(b)(5), there is a limit of one sequential Phase II award per firm per topic. Additionally, in accordance with STTR Policy Directive section 7(i)(1), each award may not exceed the award guidelines (currently $150,000 for Phase I and $1 million for Phase II, excluding DTA) by more than 50% (SBIR/STTR program funds only) without a specific waiver granted by the SBA. Therefore, the maximum proposal/award amounts including all options (less DTA) are $225,000 for Phase I and $1,500,000 for Phase II (unless non-SBIR/STTR funding is being added).

TOPIC AWARD BY OTHER THAN THE SPONSORING AGENCYDue to specific limitations on the amount of funding and number of awards that may be awarded to a particular firm per topic using SBIR/STTR program funds (see above), Head of Agency Determinations are now required (for all awards related to topics issued in or after the SBIR 13.1/STTR 13.A solicitation) before a different agency may make an award using another agency’s topic. This limitation does not apply to Phase III funding. Please contact the original sponsoring agency before submitting a Phase II proposal to an agency other than the one that sponsored the original topic. (For DON awardees, this includes other DON SYSCOMs.)

TRANSFER BETWEEN SBIR AND STTR PROGRAMSSection 4(b)(1)(i) of the STTR Policy Directive provides that, at the agency’s discretion, projects awarded a Phase I under a solicitation for STTR may transition in Phase II to SBIR and vice versa. A firm wishing to transfer from one program to another must contact its designated technical monitor to discuss the reasons for the request and the agency’s ability to support the request. The transition may be proposed prior to award or during the performance of the Phase II effort. Agency disapproval of a request to change programs will not be grounds for granting relief from any contractual requirements. All approved transitions between programs must be noted in the Phase II award or an award modification signed by the contracting officer that indicates the removal or addition of the research institution and the revised percentage of work requirements.

ADDITIONAL NOTES

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Human Subjects, Animal Testing, and Recombinant DNA. Due to the short time frame associated with Phase I of the STTR process, the DON does not recommend the submission of Phase I proposals that require the use of Human Subjects, Animal Testing, or Recombinant DNA. For example, the ability to obtain Institutional Review Board (IRB) approval for proposals that involve human subjects can take 6-12 months, and that lengthy process can be at odds with the Phase I goal for time to award. Before the DON makes any award that involves an IRB or similar approval requirement, the proposer must demonstrate compliance with relevant regulatory approval requirements that pertain to proposals involving human, animal or recombinant DNA protocols. It will not impact the DON’s evaluation, but requiring IRB approval may delay the start time of the Phase I award and if approvals are not obtained within two months of notification of selection, the decision to award may be terminated. If the use of human, animal, and recombinant DNA is included under a Phase I or Phase II proposal, please carefully review the requirements at http://www.onr.navy.mil/About-ONR/compliance-protections/Research-Protections/Human-Subject-Research.aspx. This webpage provides guidance and lists approvals that may be required before contract/work can begin.

Government Furnished Equipment. Due to the typical length of time for approval to obtain Government Furnished Equipment (GFE), it is recommended that GFE is not proposed as part of the Phase I proposal. If GFE is proposed and it is determined during the proposal evaluation process to be unavailable, proposed GFE may be considered a weakness in the proposal.

International Traffic in Arms Regulation (ITAR). For topics indicating ITAR restrictions or the potential for classified work, there are generally limitations placed on disclosure of information involving topics of a classified nature or those involving export control restrictions, which may curtail or preclude the involvement of universities and certain non-profit institutions beyond the basic research level. Small businesses must structure their proposals to clearly identify the work that will be performed that is of a basic research nature and how it can be segregated from work that falls under the classification and export control restrictions. As a result, information must also be provided on how efforts can be performed in later Phases if the university/research institution is the source of critical knowledge, effort, or infrastructure (facilities and equipment).

Partnering Research Institutions. The Naval Academy, the Naval Postgraduate School and other military academies are government organizations but now qualify as partnering research institutions. However, DON laboratories DO NOT qualify as a research partner. DON laboratories may be proposed only IN ADDITION TO the partnering research institution.

PHASE II GUIDELINESAll Phase I awardees will be allowed to submit an Initial Phase II proposal for evaluation and selection. The Phase I Final Report, Initial Phase II Proposal, and Transition Outbrief (as applicable) will be used to evaluate the offeror’s potential to progress to a workable prototype in Phase II and transition technology in Phase III. Details on the due date, content, and submission requirements of the Initial Phase II Proposal will be provided by the awarding SYSCOM either in the Phase I contract or by subsequent notification. NOTE: All SBIR/STTR Phase II awards made on topics from solicitations prior to FY13 will be conducted in accordance with the procedures specified in those solicitations (for all DON topics, this means by invitation only).

The DON typically awards a cost plus fixed fee contract for Phase II. The Phase II contracts can be structured in a way that allows for increased funding levels based on the project’s transition potential. To accelerate the transition of SBIR/STTR-funded technologies to Phase III, especially those that lead to Programs of Record and fielded systems, the Commercialization Readiness Program was authorized and created as part of section 252 of the National Defense Authorization Act of Fiscal Year 2006. The statute

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set-aside is 1% of the available SBIR funding to be used for administrative support to accelerate transition of SBIR-developed technologies and provide non-financial resources for the firms (e.g. the DON’s SBIR/STTR Transition Program (STP)).

PHASE III GUIDELINESA Phase III SBIR/STTR award is any work that derives from, extends, or completes effort(s) performed under prior SBIR/STTR funding agreements, but is funded by sources other than the SBIR/STTR programs. Thus, any contract or grant where the technology is the same as, derived from, or evolved from a Phase I or a Phase II SBIR/STTR contract and awarded to the firm that was awarded the Phase I/II contract is a Phase III contract. This covers any contract/grant issued as a follow-on Phase III award or any contract/grant award issued as a result of a competitive process where the awardee was an SBIR/STTR firm that developed the technology as a result of a Phase I or Phase II contract. The DON will give Phase III status to any award that falls within the above-mentioned description, which includes assigning SBIR/STTR Data Rights to any noncommercial technical data and/or noncommercial computer software delivered in Phase III that was developed under SBIR/STTR Phase I/II effort(s). Government prime contractors and/or their subcontractors follow the same guidelines as above and ensure that companies operating on behalf of the DON protect the rights of the SBIR/STTR firm.

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NAVY STTR 17.B Topic Index

N17B-T031 Materials Modeling Tool for Alloy Design to Streamline the Development of High Temperature, High-Entropy Alloys for Advanced Propulsion Systems

N17B-T032 Techniques to Adjust Computational Trends Involving Changing Data (TACTIC-D)N17B-T033 Optimized Build Plate Design Tool for Metal Laser Powder Bed Additive ManufacturingN17B-T034 Risk-Based Unmanned Air System (UAS) Mission Path Planning CapabilityN17B-T035 Mission Success Assessment and Mitigation Recommendations Using a Cognitive System

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NAVY STTR 17.B Topic Descriptions

N17B-T031 TITLE: Materials Modeling Tool for Alloy Design to Streamline the Development of High Temperature, High-Entropy Alloys for Advanced Propulsion Systems

TECHNOLOGY AREA(S): Materials/Processes

ACQUISITION PROGRAM: Joint Strike Fighter (JSF)

OBJECTIVE: Develop a materials modeling tool for alloy design to streamline development of high temperature, high-entropy alloys for advanced propulsion systems.

DESCRIPTION: The temperature capability of Ni-base superalloy blades has increased by more than 300°C over the last 50 years [Ref 1] and is approaching 1100°C for single crystal superalloys. In spite of many efforts, however, a further improvement in their capability is becoming more difficult due to the low melting point of Ni, which is 1453°C. Considering the ever-increasing demands for materials with higher temperature capabilities to be used in gas turbines with higher efficacy, it is of vital importance to search for alloys based on the concept of High Entropy Alloy (HEA) development.

In general, the concept is based on the idea of producing bulk crystalline alloys composed of multiple components being added in proportion that are far beyond their binary solid solubility limits, yet yielding a single-phase solid solution [Refs 1, 2, 3]. In some cases, the solid solutions formed possess simple crystal structures, such as face-centered cubic (FCC) and body-centered cubic (BCC) [Refs 4, 5], and also fulfill the expectations of combining high strength with good ductility [Ref 6]. However, successful efforts with experimental verification have not been reported in the literature. To enable rapid transition of HEAs with higher temperature capability, an innovative modeling tool for high-entropy alloy design that will enable streamlining towards rapid-alloy screening and property-orientation design is needed.

This tool must be able to predict the composition of high temperature HEAs for both equiatomic and non-equiatomic formulations for advanced Mo-Si-B alloys. Algorithms should predict microstructural characteristics such as phase evolution, grain size, grain orientation, and microstructural texture. The results of the analysis should be displayed in a graphical way that allows for understanding the new HEA’s compositions easily.

PHASE I: Design, develop and demonstrate the feasibility of algorithms to predict composition of a known high-temperature, high entropy alloy Mo-Si-B. This will include both equiatomic as well as non-equiatomic formulations. Algorithms should include phase evolution, grain size, grain orientation, and microstructural texture.

PHASE II: Down select to one composition (equiatomic or non-equiatomic) for verification through physical comparison between algorithms developed HEA and non-HEA coupons. Investigation should include the microstructural/structural changes related to various thermal processing, deformation mechanisms (room-temperature and high-temperature creep), and thermal stability/oxidation mechanisms under isothermal and cyclic exposures at elevated temperature for the selected composition.

PHASE III DUAL USE APPLICATIONS: Fully develop a materials modeling tool based upon verified algorithms. Demonstrate and validate the modeling tool against existing high temperature alloys. Transition the modeling tool for use in the development of new HEAs for advanced propulsion systems.

The technology developed will have applicability to commercial and military aviation manufacturing firms including alloy manufacturers, casting, and forging companies. Private Sector Commercial Potential: The technology developed will have applicability to commercial and military aviation manufacturing firms including alloy manufacturers, casting, and forging companies.

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REFERENCES:1. Y. Zhang, T. T. Zuo, Z. Tang, M. C. Gao, K. A. Dahmen, P. K. Liaw, Z. P. Lu (2014). Microstructure and properties of high-entropy alloys, Prog. Mater. Sci. 61, 1-93

2. C. T. Sims, N.S Stoloff, W.C. Hagel (1987). Superalloys II: High Temperature Materials for Aerospace and Industrial Power, Wiley-interscience, USA

3. C. C. Tung, J.W. Yeh, T.T. Shun, S.K. Chen, Y.S. Huang, H.S. Chen (2007). On the elemental effect of AlCoCrCuFeN high-entropy alloy system, Mater. Lett. 61

4. J.-W Yah, S.-J Lin, T.-S Chin, Y.-Y Gan, S.-K. Chen, T.-T Shun, C.-H Tsau, S.-Y Chou (2004). Formation of simple crystal structure in Cu-Co-Ni-Cr Al-Ti-V alloys with multiple metallic elements, Metall. Mater. Trans. A35 (2004) 2533-2536

5. E. Cantor, J. T.H Chang, P. Knight, A. J. B Vincent (2004). Microstructural development in equiatomic multicomponent alloys. Mater. Sci. Eng. A 375-377

6. K. C. Pradep, N. Wanderis, P. Choi, J. Banhart, B. S. Murty, D. Raabe (2013). Atomic scale compositional characterization of a nanocrystalline AlCrCuFeNiZn high-entropy alloy using atom probe tomography, Acta. Mater. 61

KEYWORDS: high entropy alloy; modeling; super alloys; gas turbines; propulsion materials; high temperature alloy

Questions may also be submitted through DoD SBIR/STTR SITIS website.

N17B-T032 TITLE: Techniques to Adjust Computational Trends Involving Changing Data (TACTIC-D)

TECHNOLOGY AREA(S): Air Platform, Battlespace, Human Systems

ACQUISITION PROGRAM: PMA-205 Naval Aviation Training Systems

OBJECTIVE: Develop technology based on statistical or computational methods to assist in the continued tracking of training performance and proficiency trends as underlying tactical data changes.

DESCRIPTION: There is a push by the DoD and USN to leverage the benefits of qualitative analysis by consuming large data sources (e.g., aviation data logs) and implementing human performance assessment and tracking of tactically relevant data to better understand force proficiency. To support decision making, big data analytics focused on developing trends or predictions based on historical data is desired. Military domains for big data is unique in that the tactics, techniques and procedures used by the fleet shift over time due to changes in capabilities or the need to adapt to novel or updated tactics by opposing forces. This creates a unique challenge for the typical statistics that would be leveraged in big data sources, as taking these changes into account is necessary to ensure that comparisons remain meaningful. The continued push for integrated warfare will likely result in cross-platform, mission-based trends; however, there may be differences in constructs across platforms (e.g., one platform may rely on timeliness and another on accuracy) that if not accounted for in the analysis or development of common construct definitions would skew analysis results. This effort seeks to identify statistical or computational methods that can assist with these adjustments to statistical trends, and implement them in an automated tool that will allow for the timely and continued calculation of trends related to fleet performance and proficiency.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense

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Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DSS and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.

PHASE I: Refine or develop methods for adjusting calculations as data points related to tactics, techniques and procedures change. Test the feasibility of implementing any identified/developed methods and to identify the benefits and limitations of each.

PHASE II: Implement automated support through algorithms or other computational processes for implementing feasible methods for adjusting data. Develop usable computer interfaces that allow end users to make note of data points being adjusted as time shifts. Ensure that data results identify any potential limitations of calculations based on early methodological testing to ensure decision makers understand the comparisons. Implement a safeguard that alerts users to the extent to which trend analysis can be continued before the comparisons are meaningless due to lack of continuity of data sources, and implement tools to assist users with re-base lining data in these situations.

PHASE III DUAL USE APPLICATIONS: Extend the baseline functionality to include advanced or more robust data analysis techniques, and/or integrate developed capability with existing database and analysis systems. Implement Risk Management Framework (RMF) guidelines to support information assurance compliance, including updates to support installation on stand alone or Navy Marine Corps Intranet (NMCI) systems. Coordinate with partners or customers of commercial applications of the technology solution developed.

Big data analytics has been implemented in a range of other domains such as athletics and medical communities. For the latter or other quickly advancing domains due to the pace at which technology support changes, novel techniques developed under this topic or integration of technology solutions such as those proposed here may provide unique insights for other domains leverage big data analytics. Private Sector Commercial Potential: Big data analytics has been implemented in a range of other domains such as athletics and medical communities. For the latter or other quickly advancing domains due to the pace at which technology support changes, novel techniques developed under this topic or integration of technology solutions such as those proposed here may provide unique insights for other domains leverage big data analytics.

REFERENCES:1. Big Data, new epistemologies and paradigm shifts: http://bds.sagepub.com/content/1/1/2053951714528481.full.pdf+html

2. Challenges of Big Data Analysis: http://nsr.oxfordjournals.org/content/1/2/293.short

3. Example commercial off the shelf technologies: http://www.predictiveanalyticstoday.com/bigdata-platforms-bigdata-analytics-software/#content-anchor

4. Challenges and Opportunities with Big Data: http://dl.acm.org/citation.cfm?id=2367572

KEYWORDS: qualitative analysis; big data analysis; human performance assessment; data trends; data predictions; statistical analysis

Questions may also be submitted through DoD SBIR/STTR SITIS website.

N17B-T033 TITLE: Optimized Build Plate Design Tool for Metal Laser Powder Bed Additive Manufacturing

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TECHNOLOGY AREA(S): Materials/Processes

ACQUISITION PROGRAM: PMA-275 V-22 Osprey

OBJECTIVE: Develop a software tool capable of optimizing the build plate design for metal powder bed additive manufacturing (AM) systems based on part geometry and features, part location and orientation with respect to the build plate and build direction, as well as the thermal effects inherent in AM. The part’s location, orientation, and support structure will be optimized to minimize induced residual stress, control geometric distortion, effectively manage heat dissipation, and mitigate the effort needed in post-process support removal.

DESCRIPTION: Additive manufacturing (AM) processes are a class of manufacturing techniques which build components from the ground up by selectively adding material in layers rather than removing or deforming bulk material. This allows for increased flexibility in part design, but also introduces additional challenges in terms of build planning. Due to the layer-wise character of AM processes, portions of the final part may not be self-supporting during the manufacturing process given the part’s features and orientation. In such cases, supporting structures must be printed only to be removed in an additional manufacturing step to achieve design geometry. Additionally, the significant thermal effects inherent in AM can lead to distortion and cracking as a result of high residual stresses if the part’s orientation and location on the build plate are not carefully considered.

Current techniques for generating support structure rely on iterating predefined support topologies, such as hexagonal honey combs, which are defined by the designer or selected by the AM machine when the toolpath is generated. This approach is primarily focused on minimizing the size and amount of support structure used. Part location and orientation are typically selected based on operator judgment and experience, or are overlooked entirely. Inadequate build plate design may result in failures during manufacture or final parts that do not meet geometric requirements, increasing time and costs as parts must be rebuilt.

To address these issues, a robust build plate design optimization tool is sought. This tool should take into consideration a part’s geometry and features, its location and orientation with respect to the build plate as well as the build path, and the characteristic thermal effects of the AM process that drive the formation of residual stresses and lead to unwanted distortion. The optimization tool should be able to provide the instructions necessary for the layout and orientation of parts on a build plate as well as the design and placement of support structure to minimize induced residual stress, control geometric distortion, effectively manage heat dissipation, and mitigate the effort needed in post-process support removal.

PHASE I: Demonstrate feasibility of a build plate design optimization tool by providing a sample build plate layout and support design for a complex geometry (e.g. overhangs, internal features, thin walls, holes/cylinders, etc.) and compare to the default or traditional build plate layout and support structure design in terms of induced residual stress, distortion, and removal difficulty using a single AM system.

PHASE II: Develop a prototype of the tool using the framework developed in Phase I optimizing the build plate design to minimize induced residual stress, control geometric distortion, effectively manage heat dissipation, and mitigate the effort needed in post-process support removal. Demonstrate that the optimized build plate layout and support structure design successfully minimized induced residual stress, part deformation, and necessary support structure as well as improved the retention of critical part features for one or more Navy-selected parts using multiple, different AM systems (i.e. different manufacturers.)

PHASE III DUAL USE APPLICATIONS: Fully develop the optimized build plate layout and support structure design tool and demonstrate it in a scenario representative of Navy implementation (i.e. using similar equipment, skillsets, and selected part(s) that would be available in a Navy application.) Transition the optimization tool into a stand-alone and/or combined product for use in Navy and commercial additive manufacturing applications.

The software tool developed through this effort will improve the quality of additively manufactured parts as well as increase the efficiency of the AM process by reducing errors and failures resulting from poor build plate design and support strategies. As these aspects are valuable to all types of AM, this toolset will be directly applicable to wide

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range of commercial applications. The proposed build plate optimization toolset would provide industry with an effective means of improving part quality during the build process. Private Sector Commercial Potential: The software tool developed through this effort will improve the quality of additively manufactured parts as well as increase the efficiency of the AM process by reducing errors and failures resulting from poor build plate design and support strategies. As these aspects are valuable to all types of AM, this toolset will be directly applicable to wide range of commercial applications. The proposed build plate optimization toolset would provide industry with an effective means of improving part quality during the build process.

REFERENCES:1. K. Mumtaz, P. Vora, N. Hopkinson (2011). A Method to Eliminate Anchors/Supports from Directly Laser Melted Metal Powder Bed Processes. Retrieved from http://sffsymposium.engr.utexas.edu/Manuscripts/2011/2011-05-Mumtaz.pdf

2. T.A. Krol, E.F. Zaeh, C. Seidel (2012). Optimization of Supports in Metal-Based Additive Manufacturing by Means of Finite Element Models. Retrieved from https://www.researchgate.net/publication/288148661_Optimization_of_supports_in_metal-based_additive_manufacturing_by_means_of_finite_element_models

3. M. Cloots, A.B. Spierings, K. Wegener (2013). Assessing New Support Minimizing Strategies for the Additive Manufacturing Technology SLM. Retrieved from https://www.researchgate.net/publication/289299663_Assessing_new_support_minimizing_strategies_for_the_additive_manufacturing_technology_SLM

4. G. Strano, L. Hao, R.M. Everson, K.E. Evans (2013). A New Approach to the Design and Optimisation of Support Structures in Additive Manufacturing. Retrieved from http://link.springer.com/article/10.1007/s00170-012-4403-x?no-access=true

5. N. Gardan (2014). Knowledge Management for Topological Optimization Integration in Additive Manufacturing. International Journal of Manufacturing Engineering. Retrieved from http://dx.doi.org/10.1155/2014/356256

KEYWORDS: cost reduction; metal additive manufacturing; part quality; support structure; residual stress mitigation; build plate design.

Questions may also be submitted through DoD SBIR/STTR SITIS website.

N17B-T034 TITLE: Risk-Based Unmanned Air System (UAS) Mission Path Planning Capability

TECHNOLOGY AREA(S): Air Platform, Battlespace

ACQUISITION PROGRAM: PMA-263 Navy and Marine Corp Small Tactical Unmanned Air Systems

OBJECTIVE: Develop an Unmanned Air System (UAS) pre-flight mission planning capability that utilizes path planning algorithms to minimize risk to personnel and property during UAS flight operations while reducing preparation times.

DESCRIPTION: The Navy continues to increase its UAS fleet with new air vehicle systems of various sizes, capabilities, and maturity. UAS do not meet the airworthiness standards that allow manned aircraft to fly within the National Airspace with minimal restrictions placed on flight plans by real-time air traffic control. As a result, UAS operations are typically limited to very restrictive operational areas (e.g. maritime operations and in Active Restricted and Warning Areas) due to risk to personnel and property on the ground. When missions necessitate operation outside of these areas, it can be particularly challenging and time-prohibitive to develop mission plans that

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ensure proper levels of safety. These constraints significantly limit Navy UAS operations for research, test and evaluation, and fleet operations; therefore, advanced and robust technologies are needed to efficiently create mission flight path plans that enable safe UAS operations within the US National and Foreign Airspaces.

Current mission path planning capabilities are primarily air vehicle (AV) centric and rely on human judgment to assess the appropriateness of a given mission plan within a broader context. The UAS air vehicle operator (AVO) is responsible for: 1) defining a complete mission plan prior to flight for autonomous execution, 2) investigating and assessing the risk to personnel and property on the ground using limited information, and 3) continuously monitoring the mission execution (and potentially intervening) for mission changes, vehicle failures, and/or airspace conflicts. The level of safety for a given mission, therefore, depends largely on an individual AVO’s ability to synthesize a myriad of data elements and promptly determine and execute the best course of action. Mission planning tools to assist the AVO in the synthesis of such data will improve the overall level of safety of UAS operations, reduce pre-flight manpower requirements, and enable broader integration of UAS within US National and Foreign Airspaces.

Mission path planning capabilities and algorithms are needed to improve and standardize the Navy’s UAS mission planning process, especially to minimize the risk to personnel and property that is independent of, and dependent on, the air vehicle. Potential technologies exist in the academic and industry communities for robotic control, machine learning, data fusion, and numerical optimization that can reduce the complexity of AVO path planning tasks. The risk-based algorithms, and associated technologies, need to be scalable from basic 2-D assessments (e.g. population data) to multidimensional optimization problems that handle mission/vehicle constraints (e.g. vehicle speeds, weight, size, maneuvering capabilities, atmospheric winds, and sensor requirements) and risk-based information uncertainties (e.g. inferring population densities from FAA Sectionals). Initial algorithm development may start with analysis of alternatives, and/or generic algorithm class representations to support the further development of the chosen technologies. The algorithm(s) will address flight path safety during normal flight and during contingency operations, including robustness to air vehicle failures and risk-based data uncertainties (e.g. population density data).

PHASE I: Develop a risk-based UAS mission path planning capability using innovative algorithm(s) for pre-flight (non-real-time) planning tasks that addresses the latitude/longitude 2-D risk problem for personnel and property on the ground. Develop a visualization method to represent the optimization problem trade space and priorities. Identify available and potential information sources to build the required risk database for the proposed mission planning capability. Incorporate example data sources into an open architecture database format and interface to run preliminary, risk-minimized path planning example scenarios by the end of Phase I.

PHASE II: Develop and demonstrate prototype technology to expand the Phase I capabilities to the multidimensional risk-based mission path planning problem. Include capabilities to minimize risk while incorporating air vehicle constraints (e.g. vehicle speeds, weight, size, maneuvering capabilities, atmospheric winds, sensor requirements) and potentially competing mission parameters (e.g. fuel consumption, time to destination, no-fly zones). Include multiple risk database sources with varying levels of detail from gross information (e.g. population data) to detailed local information (e.g. on-board sensor data). Assess the technology’s performance for real-time path planning capabilities in the presence of flight path plan modification triggers like mission objectives, vehicle failures, and airspace conflicts. Identify capability limitations, restrictions, benefits, and growth opportunities for continued development and incorporation of third-party capabilities. Perform a series of integrated mission planning exercises with validation by creating comparative human operator mission plans under the same risk-based goals and assumptions.

PHASE III DUAL USE APPLICATIONS: Transition the technology to a Navy UAS (e.g. MQ-25, Triton, Fire Scout, RQ-21 Blackjack, RQ-7 Shadow, or RQ-23 TigerShark), applicable Department of Defense or US Government UAS, or other commercial UAS application.

UAS are being developed for use across the United States, and the rest of the world, for a multitude of applications: police surveillance, package delivery, movie/TV industry, news, sporting events, recreational and business video recording, and weather monitoring. With an understanding that UAS have safety shortcomings in comparison with manned aircraft, the resulting risk to the population may be mitigated through path planning that minimizes

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exposure. Risk-based mission planning needs to be used to increase the safety UAS operations to the general population as the systems become more pervasive throughout our communities. Private Sector Commercial Potential: UAS are being developed for use across the United States, and the rest of the world, for a multitude of applications: police surveillance, package delivery, movie/TV industry, news, sporting events, recreational and business video recording, and weather monitoring. With an understanding that UAS have safety shortcomings in comparison with manned aircraft, the resulting risk to the population may be mitigated through path planning that minimizes exposure. Risk-based mission planning needs to be used to increase the safety UAS operations to the general population as the systems become more pervasive throughout our communities.

REFERENCES:1. Gonzalez, Luis Felipe, Lee, Dong Seop, and Periaux, Jacques (December 2-4, 2009). Optimal Mission Path Planning (MPP) for an Air Sampling Unmanned Aerial System, Australasian Conference on Robotics and Automation (ACRA), Sydney, Australia. Retrieved from http://www.araa.asn.au/acra/acra2009/papers/pap107s1.pdf

2. Griner, Alina (2012). Human-RRT collaboration in Unmanned Aerial Vehicle mission path planning, MIT Dept. of Electrical Engineering and Computer Science, Cambridge, MA. Retrieved from http://dspace.mit.edu/handle/1721.1/76913?show=full; DoD Defense Science Board (July 2012).

3. Task Force Report: The Role of Autonomy in DoD Systems, Office of the Under Secretary of Defense for Acquisition, Technology and Logistics, Washington, D.C. Retrieved from http://fas.org/irp/agency/dod/dsb/autonomy.pdf;

4. Rudnick-Cohen, Herrmann, and Azarm (2015). Risk-based Path Planning Optimization Methods for UAVs Over Inhabited Areas, Computers and Information in Engineering Conference, IDETC/CIE 2015, Boston, MA. Retrieved from http://www.isr.umd.edu/~jwh2/papers/Rudnick-Cohen-DETC2015-47407.pdf;

5. Tompkins, Paul (May 2005). Mission-Directed Path Planning for Planetary Rover Exploration, Tech. Report CMU-RI-TR-05-20, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. Retrieved from http://www.ri.cmu.edu/pub_files/pub4/tompkins_paul_2005_1/tompkins_paul_2005_1.pdf

KEYWORDS: mission planning; risk reduction; UAS; safety; guidance and control; airworthiness.

Questions may also be submitted through DoD SBIR/STTR SITIS website.

N17B-T035 TITLE: Mission Success Assessment and Mitigation Recommendations Using a Cognitive System

TECHNOLOGY AREA(S): Air Platform, Human Systems, Information Systems

ACQUISITION PROGRAM: PMA281 (UAS) Strike Planning & Execution Systems

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 5.4.c.(8) 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.

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OBJECTIVE: Develop a cognitive system as a selectable Unmanned Aircraft System Control Segment (U CS) Service with potential application in Naval Air Systems Command (NAVAIR) Common Control System (CCS).

DESCRIPTION: The demands on unmanned vehicle operators are increasing with the evolution of autonomous vehicles. As the CCS evolves, it is expected that one operator may need to manage a large number of autonomous manned and unmanned vehicles of varying capabilities and vehicle management needs. It is important that the operator knows where, how and when to place attention on needed issues during the execution of an integrated mission plan, especially where multiple vehicles are making decisions autonomously – without operator approval, or management by negation. Because of these newly anticipated demands on the operator, there is a need to develop a cognitive system that is offered as a UCS Service that is assessing mission risk with some form of statistical confidence to help ensure mission success within this challenging environment. This cognitive system, should support the operator in knowing where, how and when to intercede with autonomous operations to ensure mission success when controlling multiple/diverse vehicles within a theater of operations, even when the theater of operations is fluid and demanding.

In order to determine if a cognitive system is “best in class” with regard to providing a UCS service, the following is provided as the currently identified, but not necessarily all, the criteria that will be considered with regard to autonomous control systems (ACS) and CCS:1. How well does the cognitive system candidate conform to ACS based on the real time control system architecture?2. How difficult/easy can the cognitive system candidate be used within the UCS architecture?

With regard to analyzing the cognitive system’s quality of design to support “best in class” determination, the following is provided as the currently identified, but not all of, the criteria that will be considered:1. From what source was the knowledge acquisition process to develop the cognition performed?a. Were multiple sources used?b. Did the sources have differences in perspective?c. If so, how were they resolved to support an optimal cognitive action?2. What was the knowledge acquisition process used, including the process of translating expert knowledge to a cognitive system, network or branch structure?3. With regard to the cognitive system, how was the system tested/proven to be reliable in its assessment and recommendations for mission success?4. Is the statistical confidence associated with the cognitive system improving with increased sample size?

The cognitive system is required to demonstrate reliability in terms of mission risk assessment and providing recommendations based on that assessment for unmanned vehicles. The cognitive system should assess both individual vehicle/mission risk and collaborative vehicle/mission risk, including multiple unmanned vehicles and unmanned vehicles supporting manned aircraft, for example an MQ-25’s support of multiple F/A-18s. Included in the stages of developing the cognitive system, it is important that first a simulation and then live demonstration of the cognitive system be demonstrated with six or more autonomous, manned and unmanned vehicles. Of the six vehicles, an ultimate end goal for acceptance of the technology is that a minimum of two vehicle types carrying different sensor payloads need to be included in both the simulation and live demonstration. Threshold capability would be two or more cooperative unmanned systems; objective capability would be two or more cooperative manned and unmanned systems.

The cognitive system should be designed as a service module that can be installed within a UCS compliant environment based SAE standard AS6518 to support the control station’s three main components: (1) Vehicle Management, (2) Mission Planning, and (3) Mission Management. The cognitive system needs to store enough past flight information associated with all three components to support learning. The learning should be specifically focused on how to better determine success and assess risk from previous related and unrelated missions and platforms, without burdening overall Control Station performance.

The cognitive system should utilize UCS’s Data Distribution Services (DDS) middleware within the Control Station, including but not limited to sensor information to assess mission success from the vehicle. The cognitive system should also provide recommendations to the operator to improve mission success, along with percent of confidence

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increases or other risk assessment improvement. The recommendations should be able to be translated into UCS message commands that provide vehicle and payload management. Additionally, the cognitive system should include safety alerts and various safety or risk levels in real time and continuously during mission operations.

A goal of the UCS cognitive service is to cause a ground control station (GCS) to become an autonomous station, controlling various autonomous vehicles to successfully complete mission goals under supervision of a single operator. This autonomous GCS is envisioned to integrate available sensor information from one or more unmanned autonomous vehicles under control by the GCS. The cognitive system shall be able to assess with a level of statistical confidence whether a mission plan will be successful. The assessment should include a measure of risk associated with the mission plan. Additionally, the cognitive system should provide safety alerts to the operator based on assessment of risk along with recommendations to mitigate/resolve the safety alerts.

PHASE I: Provide a learning-based, algorithm in the form of a UCS service. The algorithm needs to be able to collect and integrate sensor information from various sources under the operator’s control. The algorithm should include the cognitive design of a learning system that uses statistical confidence to support success and risk assessment of a mission requiring a variety of unmanned vehicles. The algorithm should show how it can reliably and accurately determine the statistical confidence as to whether the mission plan will be successful, along with the degree of risk, including recommendations and safety alerts associated with each vehicle under the control of the GCS.

PHASE II: Develop and demonstrate a prototype cognitive system in the form of an UCS service within a UCS compliant GCS. The cognitive system should be able to collect available sensor information from various unmanned vehicles under the GCS’s control. The prototype demonstration should show how the cognitive system, using the algorithm developed in Phase I, can reliably and accurately determine the statistical confidence as to whether the mission plan will be successful and to what degree is the risk of failure, including recommendations and safety alerts to improve the degree of risk. Before a prototype is developed, a simulation should be developed to successfully show that the algorithm’s code has been implemented properly. It should integrate various sensor data that supports target and friendly vehicle identification tracking, mission assessment and targeting for potential kill chain solutions. Once the algorithm’s code is proven within the simulated environment, a live demonstration will be required, where one or more vehicles, controlled by a GCS running the algorithm, are following a complex mission plan. During the execution of the mission plan, data should be collected and processed by the algorithm in the form of one or more UCS services. The algorithm should be able to provide real time risk assessment and mitigation recommendations to the operator of the GCS.

PHASE III DUAL USE APPLICATIONS: Based on a successful prototype demonstration, further develop and test the cognitive system’s algorithm as demonstrated in Phase II for transition to the NAVAIR CCS program. In this phase, both a simulation and live demonstration are required that control a minimum of six vehicles following a multi-stage mission plan, where a minimum of two different vehicle types are used and the algorithm is providing real time assessments and recommendations.

This technology will benefit large delivery organizations such as United Parcel Service, FedEx, and others that focus on using autonomous unmanned air vehicle delivery of parcels and other items. The ability for a cognitive system to forecast mission success of one or more vehicles, while also making mitigation recommendations, has applications throughout aviation, robotics and unmanned systems industries, including commercial applications and other ground control systems within airports. Private Sector Commercial Potential: This will benefit large delivery organizations such as United Parcel Service, FedEx, and others that focus on using autonomous umanned air vehicle delivery of parcels and other items. The ability for a cognitive system to forecast mission success of one or more vehicles, while also making mitigation recommendations, has applications throughout aviation, robotics and unmanned systems industries, including commercial applications and other ground control systems within airports.

REFERENCES:1. RCS: The Real-time Control Systems Architecture (2011). Rep. NIST, n.d. Web. Specifically review sections on Development Support and Performance Measures. Retrieved from http://www.nist.gov/el/isd/rcs.cfm; Giordano, J., Wurzman, R (2016)

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2. Integrative Computational and Neurocognitive Science and Technology for Intelligence Operations: Horizons of Potential Viability, Value and Opportunity. Retrieved from http://www.potomacinstitute.org/steps/featured-articles/85-integrative-computational-and-neurocognitive-science-and-technology-for-intelligence-operations-horizons-of-potential-viability-value-and-opportunity

3. Ernst, R (March 2016). UCS Architecture Overview, NAVAIR presentation. Retrieved from http://www.dtic.mil/ndia/2016GRCCE/Ernst.pdf

4. Sun R (2007). Introduction to computational cognitive modeling. Retrieved from 7 December 2016 from http://www.sts.rpi.edu/~rsun/folder-files/sun-CHCP-intro.pdf

5. Forsythe, F, Giordano, J (2011). On the Need for Neurotechnology in the National Intelligence and Defense Agenda: Scope and Trajectory. Synthesis: A Journal of Science, Technology, Ethics and Policy 2 no 1, (2011): 5-8. Retrieved from http://www.synesisjournal.com/vol2_no2_t1/Forsythe_Giordano_2011_2_1.pdf

6. Unmanned Systems (UxS) Control Segment (UCS) Architecture: UCS Architecture Model. http://www.sae.org/search/?sort=date&content-type=(%22STD%22)&root-code=(%22AS6518%22)

KEYWORDS: cognitive system; UCS architecture; vehicle management; mission management; mission planning; common control system; sensor information; autonomous vehicles; risk assessment

Questions may also be submitted through DoD SBIR/STTR SITIS website.

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