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Research On Principle And Control Of Intelligent Vibration Reduction Boring Bar

Posted on:2019-07-31Degree:DoctorType:Dissertation
Country:ChinaCandidate:Q LiuFull Text:PDF
GTID:1361330548494594Subject:Mechanical Manufacturing and Automation
Abstract/Summary:PDF Full Text Request
In deep hole boring,vibration of boring bar is the key factor restricting machining quality and efficiency.Due to the existence of vibration,it is easy to produce internal hole surface defects(vibrogram,micro-crack),reduce the surface precision of workpiece,shorten the service life of tool(easy to break the edge),attenuate the precision of machine tool.When vibration is serious,it even threatens the safety of operators and machine tools.With the continuous development of science and technology,deep-hole parts have been widely used in the important fields(military industry,aerospace,energy equipment,etc.)related to national defense and people's livelihood.At present,limited by the level of manufacturing technology in our country,most of the cutting tools used in deep hole machining of key military components are imported tools,which poses a great threat to the national defense security of our country.There is a great demand for technological breakthrough in deep hole processing,and the effective control of tool vibration is the key to realize the breakthrough of deep hole machining technology.Therefore,it is of great significance to carry out the research on the principle and control method of deep hole vibration reduction cutting tool for realizing the breakthrough of deep hole vibration reduction machining technology and improving the quality and efficiency of deep hole machining.The causes of vibration in boring process are analyzed,and the influences of negative stiffness and negative damping on the boring process are revealed.Cutting force model considering tool vibration is established,and the vibration attitude and trajectory of cutter tip are obtained.The surface morphology is modeled and the influence of machining parameters and cutting tool angle on the boring process is revealed.A method to control vibration of boring bar by using dynamic vibration absorber is put forward.The dynamic model of dynamic vibration absorber is established,and the influence of external load and system parameters on vibration reduction performance is analyzed.This paper provides a theoretical basis for the design of intelligent vibration reduction boring bar and the proposal of vibration control strategy.An intelligent control method is proposed based on the theory of dynamic vibration absorption,which adjusts the vibration reduction performance of intelligent vibration reduction boring bar by intelligent control the stiffness of variable stiffness vibration absorber,and the design of intelligent vibration reduction boring bar is completed.The operational principle of variable stiffness vibration absorber is revealed based on the established dynamic model of variable stiffness vibration absorber.On the basis of the above research,the dynamic model of intelligent vibration reduction boring bar is established and the vibration reduction performance is analyzed.It is found that there are damping and non-damping areas on the amplitude ratio surface,and the optimal curve and the optimal control point are obtained.It provides a theoretical optimal solution for the intelligent vibration reduction boring bar control system.Through the analysis of vibration signal,the vibration variation law of boring bar during the whole period of boring is obtained.The evaluation index of vibration state is put forward,which provides the threshold for judging and controlling vibration state.The perceptual evaluation of vibration state and feedback of vibration reduction performance are realized.The control strategy of intelligent vibration reduction boring bar is put forward,and the practical optimal solution of intelligent vibration rreduction boring bar is solved by interval traversal.The performance of the control system is analyzed.The vibration state identification is realized based on BP neural network.Based on BP neural network optimized by genetic algorithm,intelligent learning is realized which make intelligent vibration reduction boring bar quickly find and predict the actual optimal solution,and improve the adjustment efficiency of the vibration reduction performance.On this basis,control platform has been built to provide a control system for the intelligent vibration reduction boring bar.The dynamic parameters of the intelligent vibration reduction boring bar are tested to ensure the accuracy of the parameters in the theoretical model.At the same time,the static/dynamic performance tests and steady excitation experiments are carried out to obtain the basic static/dynamic characteristics of intelligent vibration reduction boring bar.Then the boring experiments are carried out,and the influence of cutting parameters on the vibration performance is analyzed,which provides guidance for the selection of cutting parameters in practical machining.Finally,the vibration reduction performance of the intelligent vibration reduction boring bar is verified by the verification experiments.The research of this part has certain guiding significance for the use of intelligent vibration reduction boring bar.In this paper,taking the intelligent vibration reduction boring bar as the research object,and the vibration control of intelligent vibration reduction boring bar in deep hole machining is studied in depth.A new type of intelligent vibration reduction boring bar which integrates state perception,intelligent control and intelligent learning functions is proposed.The key technologies such as boring process,state perception,vibration reduction mechanism,vibration reduction performance adjustment,intelligent control strategy and intelligent learning are studied.The research results have certain guiding significance and reference value for the design and use of intelligent vibration reduction boring bar.
Keywords/Search Tags:Intelligent vibration reduction boring bar, Dynamic vibration absorber, Variable stiffness, Intelligent control strategy, Intelligent learning
PDF Full Text Request
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