Author(s)

Wu Xiaoqiang, Zhang Chunyou

Corresponding Author

Wu Xiaoqiang

Abstract

Hobbing cutting technology is of great significance for improving production efficiency and developing cleaner production. However, the choice of optimal hobbing parameters is the key to improving hobbing machining efficiency. To solve this problem, this paper proposes an optimization algorithm of hobbing cutting parameters based on particle swarm optimization support vector regression (SVR). Firstly, based on the objective function of hobbing optimization in the actual machining process, a multi-objective optimization function is set up with the feed rate and cutting speed as variables and the maximum productivity and minimum production cost as the optimization goals. Then, hobbing cutting parameters are optimized by combining particle swarm optimization and support vector regression. The actual machining tests show that the hobbing cutting parameters after optimization are longer than those before optimization. The experimental results verify the effectiveness of the parameter optimization algorithm.

Keywords

Hobbing, Cutting Parameters, Particle Swarm Optimization, Support Vector Regression, Parameter Optimization