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SBC: INTELLIGENT OPTICAL SYSTEMS, INC. Topic: N15AT008
Additive manufacturing (AM) is a very promising technique for rapid, low-cost production of aircraft parts directly from a CAD file. AM is especially appealing for complex parts that would be costly or impossible to fabricate by machining or casting. At the current time there are no reliable, cost-effective techniques to qualify the finished parts. Several government studies have noted this gap an ...STTR Phase II 2016 Department of DefenseNavy
SBC: POLARONYX INC Topic: N15AT008
This Navy STTR Phase I proposal presents an unprecedented NDI tool to support laser additive manufacturing of metal parts by using fiber laser SAW and heterodyne detection. It is the enabling technology for real time characterize the AM parts in terms of temperature, cooling rate, grain structure, and defects. A proof of concept demonstration will be carried out at the end of Phase 1.Prototypes wi ...STTR Phase I 2015 Department of DefenseNavy
SBC: Micro Cooling Concepts, Inc Topic: N21AT012
High power semiconductor devices suffer from difficulty in dissipating heat and thermal stresses. Silicon carbide-based power modules, in particular, have increasingly challenging heat loads despite their high efficiencies. In general terms, the module packaging methods used to improve cooling (moving metal heatsinks closer to the die) also increase thermal stresses, and the methods used to reduce ...STTR Phase I 2021 Department of DefenseNavy
SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC Topic: N13AT008
The Alpha STAR Corporation and the University of Akron STTR Phase II proposal will establish ASTM standards test methods to determine relevant ceramics matrix composites (CMC) delamination crack growth resistance (CGR) material properties (Mode I & Mode II)under service load temperature conditions. Phase Ii will expand on Phase I results, conclusions & recommendations. Emphasis will be on testing ...STTR Phase II 2015 Department of DefenseNavy
SBC: INTELLIGENT FUSION TECHNOLOGY, INC. Topic: N21AT016
In order to facilitate collaborative decision-making during modern surface warfare situations, locally learned knowledge among sailors and warfighters must be shared effectively in a timely manner. Current Naval approaches for collecting and sharing knowledge are inefficient and inflexible, as new contents are examined over extended timelines with no ability to dynamically update the knowledge bas ...STTR Phase I 2021 Department of DefenseNavy
SBC: CLEARCUT ANALYTICS, INC Topic: N16AT016
DeepDive is a system for extracting relational databases from dark data: the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data --- scientific papers, Web classified ads, customer service notes, and so on --- were instead in a relational database, it would give analysts access to a massiv ...STTR Phase I 2016 Department of DefenseNavy
SBC: Intelligent Automation, Inc. Topic: N14AT012
We propose the design and development of LCS radar modeling for training a radar modeling engine that capture the effects of environment, weather, jamming/interference and operator actions on radar display. The purpose of this engine is to reduce or eliSTTR Phase I 2015 Department of DefenseNavy
SBC: Intelligent Automation, Inc. Topic: N14AT012
We propose the design and development of LCS radar modeling for training a radar modeling engine that capture the effects of environment, weather, jamming/interference and operator actions on radar display. The purpose of this engine is to reduce or eliminate the need for live training by faithfully capturing the scenarios encountered by a radar operator. The primary target radars for the propose ...STTR Phase II 2016 Department of DefenseNavy
SBC: ROBOTIC RESEARCH OPCO LLC Topic: N13AT016
Robotic Research, LLC (RR) and Southwest Research Institute (SwRI) are creating a prototype Learning-based Approach for Relevant Data Extraction (LARDE). The LARDE framework is a data extraction and handling framework that can intelligently reduce the volume of raw data from on-board sensors, and organize and persistently store the reduced relevant dataset.STTR Phase II 2015 Department of DefenseNavy
SBC: Arete Associates Topic: N20AT014
Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop an interactive scenario building tool capable of generating realistic synthetic 360° videos in real-time for use in training simulators for periscope operators . We refer to this solution as RealSynth360. This novel capability will be created by combining the latest advances ...STTR Phase I 2020 Department of DefenseNavy