Study On Milling Stability Analysis And Spindle Flutter Recognition Method

One of the most important goals of the machining industry is to improve processing productivity and product quality.Chattering is one of the limiting factors that affects processing productivity and quality.The instability of machine tool processing caused by chatter vibration will lead to lower productivity and lower product quality,and even cause damage to the workpiece,tool or machine tool.The research on cutting stability and chatter recognition can help promote the development of China's cutting manufacturing industry to high-end manufacturing.This article originates from the Chongqing special project"Development and Application of Efficiency Enhancement Technology for NC Machine Tools for Processing Key Parts of Automotive Power Systems"?project number:cstc2017zdcy-zdzxX005?.This article takes the milling system of a domestic machine tool company's machining center?KMC500S U?as a research object.Through stability analysis,it studies the influence of dynamic parameters on machining stability,and provides guidance for the selection of cutting parameters before machining.At the same time,the vibration signals of the main shaft of the milling system are collected,and the characteristic quantities of the three states of the main shaft stabilization-transition-flutter are extracted,and the state recognition of these three states is studied.It has important practical significance for optimizing the process parameters,realizing high-efficiency processing and improving the level of milling technology.The main research contents of the paper include the following:?1?According to the flutter mechanism,the dynamic model of the milling system is studied,the dynamic differential equation is established,and the stability domain is solved by the frequency domain method.Use the modal test hammering method to obtain the natural frequency ?_n of the machining center tool system,the damping ratio? of the vibration system and the modal stiffness k.Establish the milling coefficient identification model,and obtain the tangential force coefficient K_tc and radial force coefficient K_rcc through DEFORM-3D milling force simulation experiment.?2?Use MATLAB to draw the critical stability diagram of the milling chatter system,the obtained stability diagram provides a basis for the reasonable selection of the processing parameters.At the same time,the effects of natural frequency?_n,damping ratio? and modal stiffness k on machine tool flutter stability are studied.?3?A vibration data acquisition system was established based on LabVIEW,which collected 180 sets of vibration data under different working conditions of the spindle of the machining center,and denoised the signal by wavelet soft threshold noise reduction method.The denoised signal is extracted by wavelet packet energy information entropy method and bispectrum analysis diagonal slice energy method to extract two feature quantities of the signal.?4?Using feature information wavelet packet energy information entropy and bispectrum analysis to perform two feature fusions on corner slice energy values,input support vector machine?Support Vector Machine,SVM?training and identify the spindle state.Particle swarm optimization is used to find the optimal penalty factor C and kernel width parameter?to optimize the SVM classification effect,and the optimized SVM model identification method is simulated and implemented.