Optimization and Optimization Design of Gear Modification for Vibration Reduction Based on Genetic Algorithm

In order to determine the gear modification parameters accurately, genetic algorithm was introduced to reduce the fluctuation of the gear transmission error, and the gear modification parameters were optimized with high precision. The results show that the gear modification parameters determined by this method are accurate and effective, can avoid the meshing impact, and can greatly reduce the dynamic transmission error fluctuation of the gear. Compared with the direct use of finite element method for gear modification optimization, the calculation time of this algorithm is reduced from 26.81 d to 2.24 h. Genetic algorithm is used to optimize the weights of the hidden layer and output layer of the radial basis neural network. Finally, genetic algorithm is used to rationally select the crossover probability and mutation probability to optimize the gear design. By analyzing the influence of each design variable on the error fluctuation of gear transmission, the optimal combination of design variables is obtained. Then the tooth surface of the gear pair is optimized twice, and the optimal modification optimization scheme is obtained. Finally, the gear contact analysis is used to verify and optimize the design of the two-stage helical gear reducer. The results show that the performance of this algorithm is better than the original genetic algorithm and the design efficiency is high.