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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

Download all SBIR.gov 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.

  1. Accelerated Burn-In Process for High Power Quantum Cascade Lasers to Reduce Total Cost of Ownership

    SBC: ADTECH PHOTONICS, INC            Topic: N20BT029

    Quantum Cascade Lasers (QCLs) are one of the most versatile sources of radiation in the mid-infrared range and have found applications in a variety of fields. Despite their widespread adoption, one of the main hurdles holding QCLs back from large volume manufacturing is the large cost of ownership. While QCLs, like most semiconductor devices based on III-V compounds, can leverage the economies of ...

    STTR Phase I 2021 Department of DefenseNavy
  2. 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
  3. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  4. Additive Manufacturing of Inorganic Transparent Materials for Advanced Optics

    SBC: IRFLEX CORPORATION            Topic: N19BT028

    Additive manufacturing (AM) technology offers the capability to use multiple glass materials and to print complex freeform shape designs and gradient index (GRIN) optics. Although AM is widely used to print commercial 3-dimension metal and plastic/polymer parts, there are no viable commercial solutions for AM of inorganic transparent glasses for high-quality optical components.The proposed work wi ...

    STTR Phase I 2020 Department of DefenseNavy
  5. Indicator Yarns to Detect Degradation in Nylon Webbing

    SBC: Nanosonic Inc.            Topic: N19BT032

    Nylon webbing is used for the load bearing elements in the parachute system - harnesses and risers, and degrades with use, depending on environmental conditions and frequency of use. The environmental factor that causes the most degradation of polymers in webbing is ultraviolet (UV) irradiation; this factor, combined with repeated mechanical strain, can accelerate degradation to a point where the ...

    STTR Phase I 2020 Department of DefenseNavy
  6. Analysis and Modeling of Erosion in Gas-Turbine Grade Ceramic Matrix Composites (CMCs)

    SBC: Alpha Star Corporation            Topic: N19BT033

    A significant barrier to the insertion of ceramic matrix composite (CMC) materials into advanced aircraft engines is their inherent lack of toughness under erosion and post erosion. Our team will develop and demonstrate a physics-based model for erosion/post erosion of CMC’s at room and elevated temperatures (RT/ET). The ICME (Integrated Computational Material Engineering) Physics based Multi Sc ...

    STTR Phase I 2020 Department of DefenseNavy
  7. Innovative Multi-scale/Multi-physics Model for Surface Finish Prediction and Optimization of Metal Additively Manufactured Parts

    SBC: TECHNICAL DATA ANALYSIS, INC.            Topic: N19BT034

    In this STTR effort, TDA and its team partner University of Louisville will focus on developing an innovative intelligent decision support tool using data-driven multi-scale multi-physics models (DDMM) to derive process-surface roughness relationships for selective laser melting (SLM). The proposed models account for both powder characteristics and AM processing/path planning, including powder siz ...

    STTR Phase I 2020 Department of DefenseNavy
  8. Machine Learning Tools to Optimize Metal Additive Manufacturing Process Parameters to Enhance Fatigue Performance of Aircraft Components

    SBC: TECHNICAL DATA ANALYSIS, INC.            Topic: N20AT002

    In this SBIR effort, TDA and its team partners propose to develop a comprehensive toolset based on an Integrated Computational Material Engineering (ICME) framework using Machine Learning (ML) and Artificial Intelligence (AI) algorithms to predict mechanical performance and fatigue life in additively manufactured (AM) metallic components. The toolset addresses fatigue contributing factors, includi ...

    STTR Phase I 2020 Department of DefenseNavy
  9. Hexahedral Dominant Auto-Mesh Generator

    SBC: Hypercomp, Inc.            Topic: N20AT004

    The objective of our proposed STTR phase-I work is to transition the latest advancements within the academic community to the design of a robust, user-friendly, and application-oriented tool for automatic hex-dominant meshing. Our software will fully couple CAD models to the discretized domain required by finite element software in structural analysis and other simulation and modeling applications ...

    STTR Phase I 2020 Department of DefenseNavy
  10. Hexahedral Dominant Auto-Mesh Generator

    SBC: M4 ENGINEERING, INC.            Topic: N20AT004

    Advances in both software and computer hardware have made the finite element method the preeminent choice for analyzing highly complex systems that are of great value to the Department of Defense.   The US Defense industry, however, continues to spend enormous time and resources in mesh generation, a key step in finite element analysis, despite progress that has been made in automated mesh gener ...

    STTR Phase I 2020 Department of DefenseNavy
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