Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm

Abstract

Cutting parameters have a significant impact on the machining effect. In order to reduce the machining time and improve the machining quality, this paper proposes an optimization algorithm based on Bp neural network-Improved Multi-Objective Particle Swarm (Bp-DWMOPSO). Firstly, this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm. Secondly, the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established. Finally, the Bp-DWMOPSO algorithm is designed based on the established models. In order to verify the effectiveness of the algorithm, this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control (CNC) turning machining case and uses the Bp-DWMOPSO algorithm for optimization. The experimental results show that the Cutting speed is 69.4 mm/min, the Feed speed is 0.05 mm/r, and the Depth of cut is 0.5 mm. The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality. This method provides a new idea for the optimization of turning machining parameters.