基于形态分量分析的高速铣削加工刀具磨损在线监测
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摘要
在高速铣削加工中,铣刀在超高转速下进行不连续切削,刀具磨损迅速且难以监测,严重影响加工精度和产品质量,因此刀具磨损的状态监测极其重要.振动法是一种有效的刀具状态监测方法,但是振动信号包含了多种振动成分及大量噪声,影响刀具磨损状态监测的准确性.针对该问题,提出了一种利用对偶基追踪算法和用形态分量分析对振动信号进行稀疏分解的方法.首先,分析了高速铣削加工过程中振动信号的形态分量特点和稀疏特性,构造了对偶基追踪框架,通过增广拉格朗日变量分离算法进行求解,实现对振动信号中的脉冲成分和谐波成分的分离.其次,构造并提取了脉冲密度和高次谐波频率与基频的幅值比等特征并利用这些特征进行刀具磨损状态监测.最后,通过仿真分析和实验,验证了该方法的可行性.
In high-speed milling,the cutter undergoes ultra-high-speed milling discontinuously,leading to rapid tool wear or breakage,which is difficult to monitor and will seriously affect machining accuracy and product quality,which underscores the importance of tool wear condition monitoring.Although the vibration method is an effective tool condition monitoring method,the vibration signal contains a variety of components and much noise,which decrease the accuracy of tool wear condition monitoring.To solve this problem,a sparse decomposition method of vibration signal was proposed based on the dual basis pursuit algorithm and morphological component analysis.First,morphological and sparse characteristics of thevibration signals in high speed milling were analyzed,and a dual basis pursuit framework was constructed and solved by an augmented Lagrangian variable splitting,thus separating the impulse components and harmonic components.Subsequently,two feature vectors,including the impulse density and amplitude ratio of the higher harmonics and fundamental frequency were constructed and extracted to perform tool wear monitoring.Finally,the feasibility of this method was verified through simulation and experiment analysis.
In high-speed milling,the cutter undergoes ultra-high-speed milling discontinuously,leading to rapid tool wear or breakage,which is difficult to monitor and will seriously affect machining accuracy and product quality,which underscores the importance of tool wear condition monitoring.Although the vibration method is an effective tool condition monitoring method,the vibration signal contains a variety of components and much noise,which decrease the accuracy of tool wear condition monitoring.To solve this problem,a sparse decomposition method of vibration signal was proposed based on the dual basis pursuit algorithm and morphological component analysis.First,morphological and sparse characteristics of thevibration signals in high speed milling were analyzed,and a dual basis pursuit framework was constructed and solved by an augmented Lagrangian variable splitting,thus separating the impulse components and harmonic components.Subsequently,two feature vectors,including the impulse density and amplitude ratio of the higher harmonics and fundamental frequency were constructed and extracted to perform tool wear monitoring.Finally,the feasibility of this method was verified through simulation and experiment analysis.
引文
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[3]申志刚,何宁.具备高适应性的高速铣削刀具磨损状态监测系统[J].南京航空航天大学学报,2013,45(1):49-54.
[4]陈侃.基于多模型决策融合的刀具磨损状态监测系统关键技术研究[D].成都:西南交通大学,2012.
[5]DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[6]STARCK J L,MOUDDEN Y,BOBIN J,et al.Morphological component analysis[C]//Proceedings of the SPIE 5914,Wavelets XI,59140Q.San Diego:SPIE Press,2011:1-15
[7]张晗,杜朝辉,方作为,等.基于稀疏分解理论的航空发动机轴承故障诊断[J].机械工程学报,2015,51(1):97-105.ZHANG Han,DU Zhaohui,FANG Zuowei,et al.Bearing fault diagnosis of aircraft engine based on sparse decomposition theory[J].Journal of Mechanical Engineering,2015,51(1):97-105.
[8]HE W P,DING Y,ZI Y Y,et al.Sparsity-based algorithm for detecting faults in rotating machines[J].Mechanical Systems and Signal Processing,2016,72:46-64.
[9]ELAD M,STARCK J L,QUERRE P,et al.Simultaneous cartoon and texture image inpainting using morphological component analysis(MCA)[J].Applied&Computational Harmonic Analysis,2005,19(3):340-358.
[10]YONG X,WARD R K,BIRCH G E.Generalized morphological component analysis for EEG source separation and artifact removal[C]//Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering.IEEE Press,2009:343-346.
[11]韩兴瑞.机床切削振动的结构动力学模型的探讨[J].机床与液压,2003,(4):161-163.
[12]CHEN SS,DONOHO D L,SAUNDERS M A.Atomic decomposition by basis pursuit[J].SIAM Review,2001,43(1):129-159.
[13]TIBSHIRANI R.Regression shrinkage and selection via the lasso[J].Journal of the Royal Statistical Society,Series B(Methodological),1996,58(1):267-288.
[14]DAUBECHIES I,DEFRISE M,MOL C D.An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J].Communications on Pure and Applied Mathematics,2004,57(11):1413-1457.
[15]AFONSO M V,BIOUCAS-DIAS J M,FIGUEIREDO M A T.Fast image recovery using variable splitting and constrained optimization[J].IEEE Transactions on Image Processing,2010,19(9):2345-2356.
[16]STARCK J L,ELAD M,DONOHO D.Redundant multiscale transforms and their application for morphological component separation[J].Advances in Imaging and Electron Physics,2004,132(4):287-348.
[17]ECKSTEIN J,BERTSEKAS D P.On the DouglasRachford splitting method and the proximal point algorithm for maximal monotone operators[J].Mathematical Programming,1992,55(1-3):293-318.