Online O-Ring Stress Prediction and Bolt Tightening Sequence Optimization Method for Solid Rocket Motor Assembly

Solid rocket motors (SRMs) are widely used as propulsion devices in the aerospace industry. The SRM nozzle and combustion chamber connected with a plugged-in structure, which makes it difficult to use existing technology investigate internal conditions of during docking assembly. unknown deformation O-ring inside groove caused by different assembly will prevent engine quality from being accurately predicted. Algorithms such machine learning can be fit mechanical simulation data create model that make predictions In this paper, prediction method uses sampled parameters boundary applies finite element (FEM) calculate stresses strains under conditions. fitted using gradient-enhanced Kriging (GEK) model, is more suitable for high-dimensional than ordinary model. A genetic algorithm (GA) conditional tabular generative adversarial networks (CTGAN) optimize improve its accuracy new incorporated. proposed not only accurate but also efficient, allowing significant reduction time. surrogate FEM possible predict real-time, making process smoother efficient. conclusion, provides promising solution challenges associated