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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

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 21 - 30 of 201422 results
  1. AI/ML for Additive Manufacturing Defect Detection

    SBC: PC Krause And 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
  2. AM4Sight: Additive Manufacturing, Model-based, Multi-resolution, Machine Learning defect risk visualization tool

    SBC: FTL Labs Corporation            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
  3. 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
  4. 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
  5. 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
  6. 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
  7. TALON: Multi-INT Metadata Extraction for Threat Detection

    SBC: VISION SYSTEMS INC            Topic: N222118

    Despite a large and growing set of available data sources across many domains, identifying and tracking developing threats in operational domains remains a critical unsolved problem. Extraction of relevant threat information and correlation across multiple modalities is required to take full advantage of the data available. VSI proposes TALON, a system for leveraging natural language descriptions ...

    SBIR Phase I 2023 Department of DefenseNavy
  8. Artificial Intelligence-Driven Multi-Intelligence Multi-Attribute Metadata Enabling All-Domain Preemptive Measures

    SBC: Sonalysts, Inc.            Topic: N222118

    Sonalysts, Inc. (Sonalysts) proposes the development and feasibility evaluation of a Threat Reduction by Integration of Metadata (TRIM) toolkit to integrate metadata from operational sources, independent of naval operational domain. Sonalysts will leverage the broad expertise of its employees (of which 40% are military veterans) and its software engineering and modeling resources to develop an ont ...

    SBIR Phase I 2023 Department of DefenseNavy
  9. Artificial Intelligence-Driven Multi-Intelligence Multi-Attribute Metadata Enabling All-Domain Preemptive Measures

    SBC: IERUS TECHNOLOGIES INC            Topic: N222118

    In response to the Navy’s need for an AI-enabled multi-attribute generation system, IERUS Technologies proposes to determine the technical feasibility, design and prototype for a system that can be fully integrated with proper associative databases to monitor and track developing activities/signals in all operational domains. This prototype architecture will extract meta-data attributes from exi ...

    SBIR Phase I 2023 Department of DefenseNavy
  10. Librarian- AI Driven Multi-Int Unifying Platform Software Tool

    SBC: MOSAIC ATM, INC.            Topic: N222118

    The number of sources and amount of data that Naval intelligence analysts are required to manually sift through in order to ensure maritime forces have both actionable intel and provide decision advantage for their commanders is daunting. Today, existing tools are time-consuming, workforce intensive, and cumbersome to process and distribute in a timely fashion. Fortunately, this is a problem that ...

    SBIR Phase I 2023 Department of DefenseNavy
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