Production of nano and coarse nitramines using in-situ sensors and AI/ML crystallization control

Currently, nitramine energetic materials have unacceptably high rework/scrap rates in several different manufacturing processes, such as dissolution, recrystallization, and slurry coating. Applied Sonics Inc. proposes to deploy a number of novel measurement probes during various manufacturing steps, analyze the data via machine learning (ML), and use the results to better control the process. For this proposed work, in-situ measurement probes will collect real-time data during nano and coarse nitramine manufacture, and machine learning algorithms will provide nearly instantaneous recommendations to the plant operators (or control system) on how to adjust their processes to target the desired properties. With optimized process control, out of spec batches can be dramatically reduced or even eliminated.