Prediction of gear ring clamping deformation based on BP neural network

HAN Jun, ZHANG Lei, DUAN Rong-xing, WANG Jing
（School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China）

Abstract: Aiming at the problem of clamping deformation of thin-walled ring gear, Abaqus finite element simulation and BP neural network technology were applied to the prediction of gear ring deformation. According to the actual machining and clamping of the gear ring, the Abaqus finite element analysis software was used to establish the relathonsilp between the simulation model of the gear ring clamping deformation, and the finite element analysis of the ring gear clamping deformation was carried out to establish the relationship between the ring gear clamping force and its radial maximum clamping deformations. Based on Abaqus finite element simulation data as training samples and test samples, with the good prediction accuracy and nonlinear generalization ability of BP neural network, BP neural network based digital model of gear ring deformation prediction was established by MATLAB neural network toolbox, and the model was tested according to the test sample, and the relative error between the predicted value and the simulated value was within 0.05%. The results indicate that the established BP neural network based digital model of gear ring deformation prediction is accurate and effective, and can provide accurate and effective data for the optimization of gear ring clamping parameters in intelligent big data processing and manufacturing environment.
Key words: gear ring; finite element simulation; BP neural network; clamping deformation prediction