Machine Learning Explainability for Solid Rocket Motor Simulation Using Shapley Additive ExPlanations

This paper investigates applying statistical learning techniques to solid rocket motor simulation. A solid rocket motor (SRM) internal ballistics tool is used to generate large databases of thrust-time profiles for various SRM grain designs. This work will focus on various star and circular perforated SRM grain designs. Ample data generated regression methods and neural networks are used to solve the regression problem for the response variables of interest. For this analysis, the maximum thrust, average thrust, burn time, and total impulse are to be studied. Once the statistical learning analysis has been performed, Shapley Additive exPlanations (SHAP) will be used to help explain the regression and neural network models. In this analysis, SHAP values are used to better explain the results of the machine learning analysis, especially the neural network results. These SHAP values show the predictor variables that have an impact on the response variables, something not typically available for neural networks. This paper will cover the data generated using the solid rocket motor 1D internal ballistics tool, the statistical learning approaches, along with the results and interpretation of the analysis.