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Artificial Intelligence for the Testing and Analysis of Energetic Fills

Description:

TECHNOLOGY AREA(S): Chem Bio_defense, Materialsweapons 

OBJECTIVE: DTRA seeks an A.I. enabled operating system to be applied to energetic materials (explosives) testing chambers for a modern, efficient, and rigorous capability to quickly and thoroughly characterize such materials and provide a database with automated analytical capability to show, e.g., historical performance trends, performance comparisons, variations in conditions between data points, estimate scaling performance, estimate data point error, etc. 

DESCRIPTION: The envisioned product should be able to communicate with and control a wide variety of instrumentation and diagnostic systems such that environmental, calibration curves, and other metadata information can be automatically logged and correlated with the data from measuring the explosive material performance. Such instrumentation may include both analog and digital components such as thermocouples, pressure transducers, spectrometers, cameras, oscilloscopes, etc. Data from all connected systems should be, or be able to be, automatically correlated with time and space information. The system should be able to monitor connected systems to allow, e.g., self-health checks or report when recalibration may be required. The developed operating system should allow automated or user-defined sequencing and test procedures to be defined and applied. The system should be able to provide automated analysis of tests to provide immediate feedback such as whether a test article fell within an expected performance range or whether all instrumentation captured an event successfully. Additionally the system should eventually be able to suggest appropriate testing to fill in data gaps in its database using rules such as from Design of Experiments. Modern data visualization techniques, such as virtual or augmented reality, should be supported such that events could be replayed with data layers from non-visual sensors available for overlay (e.g. be able to see a color layer representing pressure or temperature overlaid on a 3D movie of the event). More-over the system should be able to apply intelligent analysis to the database developed in order to predict baseline performance of new compounds, scaling performance of tested compounds, etc. The developed product should not be specific to a single chamber, but should instead be able to be applied to various chambers given appropriate setup. 

PHASE I: Design the intelligent operating system. Identify key hardware and software components and how they may be sourced or developed. Identify any technical or integration risks. Obtain or prototype significant or high risk components for testing and produce an analysis of alternatives if needed. Identify a particular chamber and instrumentation suite to be used with a phase 2 prototype system. 

PHASE II: Based on Phase I findings develop the initial system around a selected chamber and instrumentation suite. Demonstrate the performance of each component of the system (automated testing, data collection, automated analysis, data visualization, predictive analysis, etc.) with known compounds and compare results to expectations from historical data. 

PHASE III: Continue to develop and refine the Phase II product into a useful asset for DTRA. Adapt the product application for DTRA specific testing to include development or application of safety and security measures as required. Populate the database with quality data on DTRA specified compounds and confirm all of the autonomous and AI enabled capabilities are trained and working appropriately. Investigate commercialization avenues that could include other government agencies, national labs, research institutes, and defense contractors. 

REFERENCES: 

1. Mader, Charles & Crane, S.L. & Johnson, J.N. (1983). Los Alamos Explosives Performance Data.; 2. Dobratz, B.M. (1981) LLNL Explosives Handbook Properties of Chemical Explosives and Explosive Simulants. Lawrence Livermore Laboratory UCRL-52997.; 3. Nazarian, A. & Presser, C. (2013). Forensic analysis methodology for thermal and chemical characterization of homemade explosives. Thermochimica Acta, 576, 60-70.; 4. Maines, W.R., Kittell, D.E., and Hobbs, M.L. (2018). Combined Mini-Cylex & Disk Acceleration Tests in Type K Copper. Propellants, Explosives, Pyrotechnics, 43, 506-511.; 5. Le, Q. & Zoph, B. (2017). Using Machine Learning to Explore Neural Network Architecture. Google AI Blog, https://ai.googleblog.com/2017/05/using-machine-learning-to-explore.html.; 6. Chu, T. & Funke, M. (2019). The AI database is upon us. IBM Blog, https://www.ibmbigdatahub.com/blog/ai-database-upon-us.; 7. Anadiotis, G. (2018). GPU databases are coming of age. Big on Data Blog. https://www.zdnet.com/article/gpu-databases-are-coming-of-age/

KEYWORDS: Autonomy, Machine Learning 

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