Research on Geometric Error Prediction of High Speed Dry Cutting Gear Hobbing Based on MPGA and BPNN

Abstract

Abstract The geometric error of high-speed dry cutting gear hobbing is affected by many factors, such as process parameters, gear hobbing machine tools and hobs, and it is difficult to predict. This paper uses the vibration of the hob spindle to predict the geometric error, and builds a geometric error prediction model for high-speed dry cutting gear hobbing based on multiple population genetic algorithms and BP neural network. This model comprehensively considers the influence of the hob spindle speed, feed rate and hob spindle vibration on the geometric errors of high-speed dry cutting gear hobbing, which can provide a useful reference for the improvement of gear machining geometric accuracy. Through the experiment, it is concluded that adding the hob spindle vibration during the machining process as the model input can obtain more accurate prediction results of the geometric error of the hobbing machining. Compared with the traditional BP neural network prediction model, this model has better prediction ability.