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Tool wear online monitoring of high-speed milling based on morphological component analysis

  • 1.

    Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.08.010
  • Received Date: 21 October 2016
  • Rev Recd Date: 05 March 2017
  • Publish Date: 31 August 2017
    Abstract

  • Abstract

    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 the vibration 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

    Abstract

    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 the vibration 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
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    • References(17)
  • [1]
    HABER R E, JIMNEZ J E, PERES C R, et al. An investigation of tool-wear monitoring in a high-speed machining process[J]. Sensors and Actuators A: Physical, 2004, 116(3): 539-545.
    [2]
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    SHEN Zhigang, HE Ning. Tool wear condition monitoring system of high speed milling with high adaptability[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2013, 45(1): 49-54.
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    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.
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    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.
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    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.
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    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.
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    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.
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    ECKSTEIN J, BERTSEKAS D P. On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators[J]. Mathematical Programming, 1992, 55(1-3): 293-318.
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    [1]
    HABER R E, JIMNEZ J E, PERES C R, et al. An investigation of tool-wear monitoring in a high-speed machining process[J]. Sensors and Actuators A: Physical, 2004, 116(3): 539-545.
    [2]
    KIOUS M, OUAHABI A, BOUDRAA M, et al. Detection process approach of tool wear in high speed milling[J]. Measurement, 2010, 43(10): 1439-1446.
    [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.
    SHEN Zhigang, HE Ning. Tool wear condition monitoring system of high speed milling with high adaptability[J]. Journal of Nanjing University of Aeronautics and Astronautics, 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 Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators[J]. Mathematical Programming, 1992, 55(1-3): 293-318.
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