Simulation Of Dynamic Behavior And Parameters Optimization For Servo System Of NC Machine

To know well the dynamic behavior of electricity system, mechanical system and cutting process is the premise of designing and adjusting high performance servo system of NC machine and parameters optimization is the main method to make full of latent force of NC machine. This paper applies the PID parameter optimization theory, system identification theory, metal-cutting theory and optimized theory in designing AC servo system of NC machine and selecting cut parameters. The simulating and calculating are based on the MATLAB. The model of AC servo system of NC machine is built and the algorithm of PID parameter optimization is put forward in Chapter 2. After comparing the effect of three optimization rules, which are IST2E,ITAT and GISE based on the optimization result, the GISE rule is proved to be the most adaptable to optimize the PID parameters of the AC servo system of PMSM. The model of mechanical feed system of NC machine is built in Chapter 3, where the simulation experiment based on the Landau identification method is put forward to identify dynamic characteristic parameters of mechanical system. By the simulation experiment, the effect of system's parameters (stiffness and damp) on identification results is analyzed. The analysis followed indicates that the identification error decreases with the increasing of stiffness and becomes smaller when the damp is in an appropriate extent(about 0.1～0.3 Nm /( rad/s)). A model of dynamic milling process is also established in Chapter 4 which is verified by the experiments. Simulation results show that system is double-frequency vibration, which are close to free-frequency of system when system is at its stable condition, chatter is single-frequency vibration, which is smaller than free-frequency system and many system's parameters affect the cutting stability of which cutting width and mass are more sensitive factors. A model of cutting-parameter optimization of NC milling is completed in Chapter 5. By utilizing the GA algorithm, the cutting-parameters (cutting speed and feed rate) are optimized. The results show that the processing efficiency of the machine can be evidently improved through parameter optimization.