Study On Chatter Stability And Parameter Optimization In Milling Of Thin-walled Workpiece

Milling is of vital importance,since it is the main ingredient of mechanical processing.However,the method which can reach high machining efficiency as well as good surface quality simultaneously needs to be studied.Moreover,existing methods to predict surface roughness of metal milling are not accurate enough.A thin-walled workpiece is different from a thick-walled workpiece.Since stiffness of a thin-walled workpiece is very low,a thin-walled workpiece is prone to chatter.It is disadvantageous to quality of milling process.In this paper,modelling of milling force based on thin-walled workpiece is presented.Modellings to predicting chatter stability in milling of thin-walled workpiece in time domain and frequency domain based on theory of machining dynamics are also presented.Modelling of time complexity of numerical algorithm and analytical algorithm to predict chatter stability is also presented.Modelling of parallel chatter predicting algorithm is also presented.Modelling of predicting surface roughness of metal milling and multi-objective optimization taking into account the milling efficiency are presented.Eventually,effectiveness and accuracy of all theories are verified by modal test,milling force trial,chatter trial and surface roughness trial.Content of this study is presented as follow:1)First,milling force is predicted by the integral theory.Meanwhile,the deformation of thin-walled workpiece is considered.Then,this deformation can influence radical milling depth.Then,the time-various radical milling depth is used to revise the prediction of milling force.This is the modelling to predict the milling force in milling of thin-walled workpiece.Law of factors 's impact on milling force is analyzed through simulation and verified through cutting trials.2)Differential equation of thin-walled workpiece milling is established by considering time-and position-dependence feature of modal parameters of the cutter-workpiece system.The differential equation is solved numerically and analytically respectively to predict the stability border.These algorithms are verified by milling experiments.Modelling of time complexity of numerical algorithm and analytical algorithm to predict chatter stability is established and verified by computational tests.Modelling of parallel chatter predicting algorithm is established.3)Factors which can impact surface quality in milling of metal material are analyzed.Roughness of several milling trials under different machining condition is measured.Then,the experimental surface roughness data is analyzed by traditional linear regression algorithm and learning machines algorithm respectively.Then the formulas of surface roughness in milling of metal material are established.Then,these formulas are verified by milling trials.Experimental result shows that formulas established based on learning machines algorithm are more accurate than formulas established based on traditional linear regression algorithm.4)Milling parameters optimization is studied based on the targets of maximum material removal rate and smoothest surface quality.Constraints of milling parameters optimization are chatter stability,surface roughness,milling force and workpiece deformation.Five modellings of milling parameters optimization are established.Both two-stage multi-objective optimization(surface quality prior)and two-stage multi-objective optimization(material removal rate prior)performs well.Eventually,a milling simulation and analysis system is developed based on all theories in this study.