Neural network model in digital prediction of geometric parameters for relative position of the aircraft engine parts

Abstract

The quality of aircraft and rocket engines depends primarily on the geometric accuracy of assembly units and parts. Mathematical models implemented in the form of computer models are used to predict quality indicators (in particular, assembly parameters). Direct modeling of the conjugation process using numerical conjugation and finite-element models of assemblies requires significant computational resources and is often accompanied by problems convergence of solutions. In order to solve the above problems, it is possible to use neural network models describing the main regularities of the pairing process based on the accumulated results. The work presents a neural network model for predicting assembly parameters of the parts based on the use of actual surfaces of the parts obtained as a result of mathematical modeling. Assembly on conical surfaces is considered. A convolutional neural network was used to predict assembly parameters.