Spindle Power Prediction and Feed Rate Optimization based on Impeller Milling - Wang, Wei (International Journal of Mechanical and Electrical Engineering, 2025)
This study is dedicated to solving the control hysteresis problem caused by the slow response speed of traditional fixed-parameter machining and control systems, and investigates a method aimed at combining deep learning and PID controllers to achieve intelligent optimization of the feed rate for ternary impeller milling. A two-stage step-by-step prediction of the spindle power is used to take advantage of the LSTM prediction optimization solving algorithm's fast response to time series data processing, and the PID controller adjusts the feed multiplication rate by proportional, integral and differential control strategies according to the model output to achieve constant power stable cutting with pre-adjustment of feed multiplication rate for CNC milling process. The experimental results show that the intelligent optimization scheme of feed multiplication for ternary impeller milling based on deep learning and PID controller has high effect. Through continuous learning and intelligent adjustment, the machining efficiency is significantly improved, while the machining quality and stability are ensured.