No abstract

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

Aerospace engine is the source of power for the development of national defense and aerospace. Engine bolt tightening is an important part of the aerospace engine production process. At present, most of the bolt tightening methods of aerospace engines are manual tightening or traditional rigid tightening, which lead to the problem of low efficiency and large engine deformation. This paper proposes a process planning method for bolt tightening of aerospace engine based on digital twin, including flexible tightening mechanism design, tightening sequence optimization, the establishment of the digital twin system of the tightening mechanism, etc. The flexible tightening mechanism is suitable for different types of aerospace engines. Based on bolt tightening sequence optimization, the deformation and stress of the cabin section during the tightening process are reduced. The bolt tightening work is completed in the digital twin system, the real-time communication of physical and digital space synchronizes the operations of them. After tightening, the residual pre-tightening force of bolts are detected based on the ultrasonic method. The results show that the proposed method can solve the problems of low efficiency of aerospace engine bolt tightening and the problem of the cabin’s large deformation. Furthermore, it provides technical reference and theoretical basis for the bolt tightening technology of aerospace engine.