Application of singularity vibration for minimum energy consumption in high-speed milling

Abstract

To provide a model for real-time prediction of cutting forces, vibrations and optimal energy consumption (EC) model, a new hybrid algorithm based on Back-Propagation Neural Network and Multi-Objective Particle Swarm Optimization (BPNN-MOPSO) was developed to determine the optimal cutting parameters with minimal total energy consumption. High-speed milling experiments were conducted to confirm the proposed online monitoring and optimal model’s accuracy and availability. The proposed optimal method, based on the proposed improvement model, can reduce EC by 10.49% compared to the empirical option method. The feasibility and effectiveness of the proposed model have been verified through experimental processes.