A Chemometric Approach to Categorize HTPB and Reliably Predict Outcomes in Gum stock and Propellant Formulations

This Phase I proposal will utilize multivariate regression, including cluster and principal component analysis to categorize lots of HTPB R-45M based on their physicochemical properties. Artificial intelligence-driven pattern recognition analysis will be performed on a variety of HTPB characterization data and spectra. Categorized data will be assessed against measured outcomes (pot life, mechanical properties, cure time, aging behavior) for both gum stock and full propellant formulations. The result of the Base portion of our work will be at least one new tool for distinguishing seemingly identical lots of HTPB, with preliminary analysis showing correlation between distinct lots and measurable formulation outcomes. Programmatic progression to Option and Phase II portions will complete validation of the tool(s) developed during the Base portion. Phase III efforts will leverage prescriptive analytics to develop reliable process controls to produce HTPB with desired physicochemical properties at the pilot scale. The overall deliverable of this proposal is a software toolkit to predict the behavior of HTPB R-45M in energetic formulations based on polymer characterization inputs that can be measured by a typical energetics laboratory or production facility. This toolkit will allow propellant manufacturers to easily predict how a given lot of HTPB R-45M will perform in a propellant, enable the development of better specifications for HTPB R4-5M, and ultimately drive improvements in the R-45M product itself.