Research On Tool Wear And Monitoring In Milling Process

Tool wear is a common problem in the milling process,which will have a major impact on the efficiency and quality of milling.At the same time,tool wear will also have an impact on the milling force and chatter generated in the milling process.Therefore,from the perspective of the optimization of processing parameters and the online monitoring of the processing process,it is of theoretical and practical significance to study the milling force,chatter,tool wear and the relationship between them generated during the milling process.Focusing on the research on tool wear,the main research contents of this article include:(1)The modeling of milling force was studied,and the relationship between milling force and machining parameters and tool wear was explored.The traditional milling force model and linear milling force coefficient identification model were established.Through the slot milling experiment,whether the established model meets the accuracy requirements was verified.According to the established milling force model,the effects of three main processing parameters,axial cutting depth,spindle speed and feed per tooth,on milling force were analyzed and compared.Meanwhil,under the premise of keeping the processing parameters unchanged,the milling force of different wear tools was analyzed,and the influence of tool wear on the milling force was revealed from the perspective of time domain and frequency domain.(2)The algorithm of chatter prediction and the influence of tool wear on chatter were studied.Based on the improved Runge-Kutta-semi-discretization method,the chatter stability lobe diagram of the milling system was obtained by coupling the modal parameters of the tool and the workpiece.Through milling experiments and microscope observation,the preponderance of the algorithm used compared with other algorithms were verified.Meanwhile,under the premise of keeping the processing parameters unchanged,Fourier transform was performed on the milling force of different wear tools,and the affect of tool wear on chatter was studied.At the same time,based on VMD(Variational Modal Decomposition),with energy entropy as an indicator,the influence of tool wear on chatter was revealed through quantitative analysis.(3)The acquisition of tool wear data set and signal de-noising algorithm were studied.Through the tool wear experiment,the force signal and acceleration signal in the milling process were collected,and the tool wear was measured offline,thus the tool wear data set was obtained.On this basis,the wavelet threshold denoising method was deeply delved.Integrating the advantages of the semi-soft threshold method and the layered threshold method,and introducing the firefly algorithm,an innovative wavelet double threshold method based on the correlation coefficient between layers was proposed.Using signal to noise ratio(SNR),root mean square error(RMSE)and blind signal to noise ratio as indicators,the denoising processing of simulated signals and signals in the processing process proved the superiority of the proposed algorithm over traditional methods.(4)The tool wear prediction model and the establishment of online monitoring system were studied.After performing a series of processing on the force signal and milling force signal in the tool wear data set,the eigenvalues highly related to the tool wear were obtained.On this basis,the prediction accuracy of tool wear prediction models with different types of signals was studied and compared.At the same time,based on the established prediction model,an online tool wear monitoring system with signal acquisition,signal denoising,signal truncation,feature extraction and wear prediction functions was established.This paper studies the milling force control,chatter prediction and online monitoring of tool wear in the milling process,and analyzes the effects of tool wear on the milling force and chatter.The research results have great practical significance for the optimization of processing parameters and the online monitoring of the milling process.