Optimization of Gear Shaper Cutting Parameters Based on BP Neural Network and Genetic Algorithm

Abstract

Abaqus finite element simulation technology and BP neural network and genetic algorithm are applied to the study on the optimization of the cutting parameters of the thin-walled ring gear. A simulation model of ring gear shaping was established by Abaqus finite element analysis software, a cutting force prediction model was built using BP neural network. With the help of the BP neural network and genetic algorithm function extreme value optimization feature, the gear ring gear shaping parameter optimization model was established, and the Abaqus simulation model was used to test it. The relevant slotting parameters, namely the slotting speed, the amount of back-cutting and the circumferential feed speed have been successfully optimized, and the slotting force has been reduced. The experimental results show that the parameter optimization model of ring gear shaping based on BP neural network and genetic algorithm can effectively realize the optimization of slotting parameters.