Dynamic Optimization Design of Transverse Axis Cutting Reducer

Abstract

Focused on the gear transmission system of a certain type of transverse axis cutting reducer, this paper firstly establishes the ADAMS virtual prototype model for calculating the dynamic load coefficient of the system, and the influence of gear modulus and tooth width factor on the dynamic load coefficient of the system is determined by multiple linear regression. Then the GA-BP neural network model for calculating the dynamic load coefficient of the system is constructed based on Genetic Algorithm (GA) and BP neural network, which makes the decoupling of the system dynamics analysis and optimization design process be realized. Finally, based on the above analysis, a multi-objective optimization design with the goal of minimizing the system volume and dynamic load coefficient is carried out. The calculation results show that the gear transmission system of the transverse axis cutting reducer is optimized efficiently, and its volume is reduced by 18.8%, and the dynamic load coefficient is decreased by 11.5%. This paper starts with the system dynamics analysis, makes the GA-BP neural network model as the bridge, and finally establishes the technical method for dynamic analysis optimum design of the transverse axis cutting reducer. The analytical technique and method can provide reference for the optimization design analysis of complex gear transmission system.