Three-dimensional Elliptical Vibration Cutting System Modeling And Control Of Study

Elliptical Vibration Cutting is a new processing method which has been developed in recent years,it can obtain micro machining of the required cutting motion, used to improve the machinability of some of the difficult processing materials, etc.Such as brittle materials,such as optical crystal, super steel ferrite materials, these materials in precision mold parts and is widely used.Three dimensional EVC can achieve the best performance in the process, the key is still in the three-dimensional EVC device and its control.However,there are still some problems to be solved in the elimination of the motion coupling and control between the axes of the existing three-dimensional EVC devices,For example, the dynamics model of the three-dimensional EVC device, the control of the 3D ellipse motion trajectory, and the nonlinear problem of the 3D ellipse motion.This paper will mainly focus on these aspects of the in-depth study of the problem.Dynamic modeling based on the research of the flexible hinge hybrid drive three dimensional EVC device.First group is analyzed, and the three dimensional EVC device has carried on the simple introduction, and then to system dynamics modeling of simplified model device, it is concluded that to the transfer function for the third order parameter model.A system dynamics model for 3D EVC device on the specific research and analysis of the advantages and disadvantages of RBF neural network control method and adaptive sliding mode control method.Based on RBF neural network and adaptive sliding mode control strategy, an adaptive sliding mode control method based on RBF neural network is proposed,This method has the advantages of the two kinds of control strategy, and the mutual influence of a certain extent make up their own shortcomings.Based on the nonlinear characteristics of the three-dimensional elliptic vibration cutting system, the sliding mode control law is designed, and the improved control algorithm of the deep learning neural network is designed.RBF neural network adaptive sliding mode controller is used to control the system model.And the RBF neural network is adopted to transfer function are compared, and identify the exact transfer function. The design of the controller and the device model for the simulation analysis.Simulation results show that the adaptive sliding mode controller based on RBF neural network can effectively restrain the chattering problem, and has strong robustness.The NI simulation control platform to validate experiment. The first step is to build the experimental platform of control NI simulation controller, editing control program, will set good input parameters of the analog controller, collect data, input to the device model,device of the displacement tracking curve and speed tracking curve. Analysis of experimental results to verify the feasibility and superiority of the design the controller.