A Comparison Between Allied Ordnance Procedure‐48 and Various Machine Learning Models

ABSTRACT This study examines the stabilizer depletion data of seven distinct gun propellants (single base and double base, stabilized with DPA or Arkadite II) using the allied ordnance procedure (AOP)‐48 kinetic approach and three machine learning algorithms: random forest, extreme gradient boosting, and neural network. The efficacy of the various methodologies is evaluated in relation to the quantity of training data. The AOP‐48 kinetic model demonstrates optimal performance when trained on sufficiently sized datasets, accurately predicting the stabilizer content with a mean absolute error of 0.03%. The mean absolute errors achieved by the machine learning algorithms were between 0.06% and 0.15% for stabilizer content. Nevertheless, the efficacy of machine learning models can be enhanced by incorporating the propellant composition into their architecture, thereby reducing the mean absolute error to a range of 0.05%–0.075% for the stabilizer content. The impact of varying training and testing data partitions has been subjected to comprehensive analysis, and the requisite quantity of data points for developing a model yielding accurate predictions (below 0.05% error of stabilizer concentration) has been determined to approximately 30 data points per formulation, while approximately 15 points are sufficient for the AOP‐48 procedure.