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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

Download all award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

Displaying 71 - 80 of 189032 results
  1. Detailed Manufacturing Planning and Trade-off Analysis of Topology-Optimized Parts

    SBC: Medema Labs Corp            Topic: N202117

    While topology optimization can define parts that have minimal and nearly uniform strain energy, these solutions cannot be manufactured without modification. Numerous research papers have looked at ways to incorporate manufacturing constraints within the optimization. This has promise for additive manufacturing but has significant hurdles when subtractive manufacturing is used. There is not a clea ...

    SBIR Phase I 2021 Department of DefenseNavy
  2. Optimized Subtractive Manufacturing- MSC P4595

    SBC: Materials Sciences LLC            Topic: N202117

    Currently, commercially available structural optimization methods, e.g., topology, shape, etc., result in a mesh-based output.  This mesh-based output is not generally in a format directly usable to create a part by either additive manufacturing (AM) or subtractive manufacturing.  Further, optimization method that do not incorporate manufacturing constraints often result in complex geometries th ...

    SBIR Phase I 2021 Department of DefenseNavy
  3. Identifying and Characterizing Cognitive Sensor Systems in Tactical Environments

    SBC: Vadum, Inc.            Topic: N202121

    Adversary radar jamming capability will greatly increase in the near future.  Improvements in radio frequency (RF) hardware, especially in solid state RF electronics, will lead to improved jammer capability with reduced size, weight, and power requirements.  Moreover, improvements in embedded computer systems will give jammers access to powerful machine learning and artificial intelligence algor ...

    SBIR Phase I 2021 Department of DefenseNavy
  4. A Multi-Scale Model for Large Aluminum Forging Parts

    SBC: Triton Systems, Inc.            Topic: N202122

    Naval Aviation aircraft procurement faces cost and schedule challenges, largely due to high scrap rate of large airframe aluminum forging parts. The parts were rejected due to geometrical non-conformance, mostly due to distortion induced right after the quenching step. There is a need to develop a prediction tool to run simulations with optimized quenching parameters yielding least post-quenching ...

    SBIR Phase I 2021 Department of DefenseNavy
  5. Innovative Multi-Physics-based Tool to Minimize Residual Stress / Distortion in Large Aerospace Aluminum Forging Parts

    SBC: Global Engineering and Materials, Inc.            Topic: N202122

    Global Engineering and Materials, Inc. (GEM) and its team members, University of Illinois Urbana-Champaign (UIUC) and Professor Richard Sisson from the Center for Heat Treating Excellence at Worcester Polytechnic Institute (WPI), propose developing a coupled tool based on integrated computational materials engineering (ICME) and a machine learning (ML) approach to optimize quenching processes with ...

    SBIR Phase I 2021 Department of DefenseNavy
  6. QPOT: A Multiphysics Analysis based Quenching Process Optimization Tool for Large Forging Parts

    SBC: INTELLIGENT AUTOMATION, INC.            Topic: N202122

    Naval Aviation aircraft procurement faces cost and schedule challenges where one of the major contributors is the high scrap rate of large airframe aluminum forging parts. The parts were rejected for geometrical non-conformance, due to distortion induced during quenching stage. To address the critical challenge, Intelligent Automation Inc. (IAI) and collaborators propose to develop an innovative m ...

    SBIR Phase I 2021 Department of DefenseNavy
  7. Magnesium Alloys for Additive Manufacturing by Artificial Intelligence (MAGAMAI)

    SBC: INTELLIGENT AUTOMATION, INC.            Topic: N20BT026

    NAVY seeks high strength, low density, and high corrosion resistant alloys for structural components which can be processed by additive manufacturing (AM). Magnesium (Mg) alloys are candidates for fuel-efficiency applications, especially the aircrafts. They satisfy density, strength, and stiffness for many designs. However, their low corrosion resistance cannot ensure design lifetimes.  This limi ...

    STTR Phase I 2021 Department of DefenseNavy
  8. ICME-based design of Magnesium alloys for additively manufactured structures and repairs

    SBC: QUESTEK INNOVATIONS LLC            Topic: N20BT026

    Under this STTR program, QuesTek Innovations will utilize its knowledge and expertise in Integrated Computational Materials Engineering (ICME) to develop high-performance, additively manufacturable Mg alloys for application in repairs or as a lightweight structural component in aircraft. The need for accelerated development and desired performance ranging from mechanical properties, AM processabil ...

    STTR Phase I 2021 Department of DefenseNavy
  9. Lightweight Rapid Alloy Development and Fabrication using ICME based Optimization

    SBC: MRL MATERIALS RESOURCES LLC            Topic: N20BT026

    Magnesium alloys offer an attractive combination of good mechanical properties with very low density, making these materials strong candidates for structural applications such as gearboxes for rotary wing aircraft, as well as finding strong interest in the automotive industry for engine and transmission components.  Repair of such structures by additive manufacturing, as well as manufacturing of ...

    STTR Phase I 2021 Department of DefenseNavy
  10. Efficient Burn-in Process for High Power QCL Lasers

    SBC: Raytum Photonics LLC            Topic: N20BT029

    Raytum Photonics teams with the Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland in order to come up with an efficient burn-in process to effectively screen out infant mortality and accurately predict life time for QCL lasers in shortest possible time.   The proposed burn-in process is based on an accelerated degradation model which speeds up the burn-in process by el ...

    STTR Phase I 2021 Department of DefenseNavy
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