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We believe in the long run, the insights gained for this program will enable easier transition of novel energetic formulations. Beyond reducing out of spec batches, we would also like to reduce cost, environmental footprint (by mainly increasing energy efficiency and reducing solvent use), and increase throughput from existing lines. The probes will collect data in real time as materials are manufactured, and the machine learning algorithms will provide nearly instantaneous recommendations to the plant operators on how to adjust their processes to target the desired properties. We believe that by deploying a number of different measurement probes during various manufacturing steps, and analyzing the data via machine learning, we can dramatically reduce or even eliminate out of spec batches. To further exacerbate the problem, munitions’ energetics manufacturing processes are poorly understood ‘black boxes,’ so the reason behind any deviation from spec is difficult to ascertain. This is largely due to the plant operators inability to control critical manufacturing parameters such as cooling water temperature, nitramine concentration, and solvent/antisolvent ratios. TOPIC OBJECTIVE: To develop a suite of probe technology and machine learning algorithms which can be used throughout the energetics manufacturing process to reduce cost and increase product consistency.Ĭurrently, nitramine energetic materials have unacceptably high rework/scrap rates in a number of different munitions’ energetics manufacturing processes, such as dissolution, recrystallization, and slurry coating. OUSD (R&E) MODERNIZATION PRIORITY: Artificial Intelligence/Machine Learning

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