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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

Displaying 21 - 30 of 204843 results
  1. Tunable, Repeatable, Calcium Lanthanum Sulfide Ceramic Powder

    SBC: TDA RESEARCH, INC.            Topic: N222116

    There is a critical need for a high purity commercial source of calcium lanthanum sulfide (CaL2S4; CLS).  Ceramics made from CLS have an unusually broad range of transmittance in the infrared (IR), as well as high mechanical strength and environmental durability.  For these reasons, CLS is an ideal material for IR windows used in applications such as multispectral imaging.  CLS ceramics are not ...

    SBIR Phase I 2023 Department of DefenseNavy
  2. Development of Tunable, Repeatable, Calcium Lanthanum Sulfide Ceramic Powder

    SBC: Lorad Chemical Corporation            Topic: N222116

    Lorad Chemical Corporation (“Lorad”) has developed nano-scale high-purity Calcium Lanthanum Sulfide ("CLS") powders suitable for fabrication of near theoretical density infrared ("IR") transparent domes, windows, lenses, and other parts. CLS incorporated into optical components and domes / windows has several applications in IR optical sensing systems and IR lenses for laser applications. ...

    SBIR Phase I 2023 Department of DefenseNavy
  3. Defect Detection from In-situ Monitoring of LPBF Additive Manufacturing

    SBC: CFD RESEARCH CORPORATION            Topic: N222117

    Additive Manufacturing provides many potential advantages, relative to traditional manufacturing methods, for the Navy and other organizations in the aerospace community.  Although flight critical aerospace quality metal alloy components have been produced and flight tested, confidently expanding the use of AM in these applications requiring stringent quality control and repeatability. The vendor ...

    SBIR Phase I 2023 Department of DefenseNavy
  4. AI/ML for Additive Manufacturing Defect Detection

    SBC: PC KRAUSE & ASSOCIATES INC            Topic: N222117

    Metal additive manufacturing (AM), particularly, laser powder-bed fusion (LPBF) has transformative potential to achieve geometric design freedom at low production volumes. However, porosity and localized defects remain a significant challenge to implementation in mission critical aerospace applications. While the quality of LPBF is now competitive with or surpasses castings for established materia ...

    SBIR Phase I 2023 Department of DefenseNavy
  5. AM4Sight: Additive Manufacturing, Model-based, Multi-resolution, Machine Learning defect risk visualization tool

    SBC: FTL LABS CORP            Topic: N222117

    While AM systems, especially metal AM, bring revolutionary capabilities and have the potential to reduce supply chain issues and enable new designs through unique layer-by-layer fabrication capabilities, AM technologies currently suffer from defects that exist within the components. Defects such as porosity, inclusions, large-scale voids, and chemical inconsistencies can inhibit the functional per ...

    SBIR Phase I 2023 Department of DefenseNavy
  6. Multisensor Insitu Data with Machine Learning

    SBC: QUARTUS ENGINEERING INCORPORATED            Topic: N222117

    The Multisensor Insitu Data with Machine Learning (MIDML) program will develop a convolutional neural network (CNN) to leverage data generated from multiple AM inprocess sensors. This can provide more accurate and reliable assessments and predictions of final part quality during layer-by-layer fabrication, in real time. The CNN will be developed for maximum prediction accuracy from multiple senso ...

    SBIR Phase I 2023 Department of DefenseNavy
  7. Physics-enhanced AI/ML tool for additive manufacturing defect detection and prediction

    SBC: QUESTEK INNOVATIONS LLC            Topic: N222117

    Through this proposed SBIR effort, QuesTek Innovations LLC will develop a physics-enhanced machine learning (ML) software to reduce exhaustive typical on-destructive testing or enable feed forward control methodologies for fabrication of high-quality additive manufacturing (AM) component. Process induced defects like keyholing and lack of fusion porosity are shown to be strongly correlated with AM ...

    SBIR Phase I 2023 Department of DefenseNavy
  8. Artificial intelligence and machine learning algorithms to detect defects in additive manufacturing by fusing multiple sensor data

    SBC: ADDIGURU, LLC            Topic: N222117

    While additive manufacturing (AM) has enabled the fabrication of complex geometries, process repeatability, and part quality has been an inhibitor to widespread industry adoption. Parts fabricated using metal AM can have their mechanical properties compromised due to the presence of defects. Presently, quality assurance is achieved by X-ray Computed Tomography (CT), which is costly and time-consum ...

    SBIR Phase I 2023 Department of DefenseNavy
  9. Defect Detection in Metal Additive Manufacturing

    SBC: Product Innovation And Engineering, LLC            Topic: N222117

    In-process inspection offers a path forward to insuring components produced via metal additive manufacturing (AM) such as directed energy deposition (DED). However, a simple anomaly detection ML technique driven from observational data may be insufficient for tracking the performance of a system intended to produce arbitrary geometry. The methodology chosen for a defect monitoring system must be g ...

    SBIR Phase I 2023 Department of DefenseNavy
  10. PrintRite3D? AI/ML for In-Situ Additive Manufacturing Defect Detection

    SBC: KITWARE INC            Topic: N222117

    Additive manufacturing (AM) increases the speed and flexibility of production and enables traditional part concatenation for advanced manufacturing capabilities. The ability for U.S. manufacturers to 3D-print advanced components in-house reduces reliance on traditional subtractive supply chains and bolsters national security readiness. While AM affords unique flexibility in design for manufacturab ...

    SBIR Phase I 2023 Department of DefenseNavy
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