Residual Stress Prediction of Internal Helical Gear Profile Grinding Based on FEA and RBF Neural Network

As one of the most important finishing machining means of internal helical gear, the residual stress that appears during profile grinding plays an important role in transmission performance and the service internal helical gear. In this research, the residual stress simulation model of internal helical gear profile grinding is established to optimize and predict grinding parameters by means of a neural network. The grinding process parameters (including grinding depth, grinding feed speed, and grinding wheel linear speed) are taken as variable factors. Through experimental verification, the maximum error of the simulation value is 12.8%. The radial basis function (RBF) neural network is introduced, and simulation data samples are used to train and test the residual stress prediction model. Three groups of unknown grinding parameters are predicted, and the relative errors between the predicted and measured values are 5.16%, 1.63%, and 3.39%, respectively. The results demonstrate that the RBF neural network residual stress prediction model proposed in this paper is accurate and feasible. At the same time, the residual stress prediction method provides a theoretical basis for optimizing and controlling the precision of internal helical gear profile grinding.