Artificial Intelligence for the Testing and Analysis of Energetic Fills

Prescriptive and Predictive Decision Analytics have proven invaluable in helping the USAF sustain combat support goals. The challenge lies not just with the analytics but in aggregating mountains of varied data and exploiting information; data curation is the real goldmine. Humans lack the ability to provide real-time data curation of the deluge of new data. Machine Learning (ML) is now providing capabilities beyond what was previously thought possible. The USAF is looking to utilize innovative technologies to bring greater capabilities to explosives formulation/research to enable an Explosives Operations System (EOS). The need is also to capture/protect the intellectual knowledge currently centralized in a shrinking number of experts/greybeards. The goal is to provide both weapon system enhancements, reliability increases and cost benefits with a focus on establishing ML enhanced decisioning. RJLG will demonstrate a mature USAF SBIR-funded ML software technology capable of collecting, aggregating, and mapping together heterogeneous research and development data sets. The technology will host complex ML algorithms and perform all necessary data ingestions, translation and model execution functions required to enable an EOS while providing a reduced-risk and proven capability. The technology can be applied cross-industry in organizations where timely, data-driven decision making is critical.