Research On Milling Force Identification Method Based On Feed Drive Voltage And Current Signal Of CNC Machine Tool

In the actual machining process of CNC machine tools,since the machining milling force directly echo the balance of the cutting process and the thin-walled parts deform the tool,in the direction of assure machining certainty and advance machining ability,online conclusion of cutting force has become an critical research guidance in the field of flexible machining.At the same time,the wear state of the cutting tool is closely related to the tool shear force coefficient and the cutting edge force coefficient.Therefore,it is very important to identify the milling force and the milling force coefficient online.However,it is difficult to meet the requirements of actual working conditions by direct measurement using a three-dimensional dynamometer.For this reason,this dissertation analyzes the online description design of milling force and milling force factor based on the feed drive voltage and current signal of CNC machine tools.CNC machine tools are mainly composed of spindles and servo feed shafts,and the thesis focuses on the milling force identification of the voltage and current signals of the permanent magnet synchronous motor of the milling feed shaft of milling processing.Firstly,combining the mathematical model of milling force with the vector control model of the permanent magnet synchronous motor,the electromagnetic coupling control model of the feed shaft machine is built in Simulink.Then,the rapid power manner is used to forecast the solenoid torque and laziness torque of the cylinder by using the motor voltage,current signal and cylinder velocity of the feed cylinder,and then the load milling torque and milling force of the feed cylinder are forecasted by the torque equation.Finally,according to the analytical model of milling force,the continuous relapse method is used to analyze the shear force coefficient and cutting bind force coefficient.Due to the fact that the cylinder acceleration alarm is not easy to access and the identification certainty is not great in the actual machining case,the identification of the milling force must first estimate the accurate motor speed.Therefore,the Sliding Mode Observer(SMO)is used to appraisal the jagged area of the motor rotor and the mechanical jagged velocity of the rotor by using the electric machine voltage and current sign,and the sliding mode rule is enhanced,which greatly lessen the chattering phenomenon and obtains a more accurate measurement motor speed.At the same time,the improved sliding mode observer is introduced into the electromagnetic coupling control model of the feed axis machine,and the speed estimation model is established.The feasibility of the improved model is verified through the comparative analysis of reproduction.At the same time,in order to improve the accuracy of the model and reduce the impact of changes in the electrical parameters of the motor on the estimation of motor speed,the recursive Least Squares method(RLS)is used to identify the electrical parameters such as the stator resistance and inductance of the motor,and feedback them to the improved sliding mode observer to improve the accuracy of parameter identification.Combining the advantages of RLS and SMO,on the basis of the electromagnetic coupling control model,the motor parameter identification strategy based on RLS-SMO is adopted to realize multi-parameter online identification based on feed shaft motor voltage and current signals.Finally,several sets of cutting experiments were designed,and the voltage,current and milling force signals of the machine tool feed axis motor were collected.Using the above method,the rotational speed of the feed shaft driving motor was estimated,and the electrical parameters of the feed shaft motor were identified.At the same time,the instantaneous power plan and the average milling force plan were used to classify the milling force waveform and the milling force coefficient.Compared with the reproduction content,the usefulness of the proposed method is documented.