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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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