对介观尺度下三向正交的高速微铣削力混合信号提出一种有效的分离算法，从而获得高速微铣削力的真实信号及机床振动信号并基于此对机床状态进行分析。算法首先以信息量为处理标准将观测信号初步分离，再将分离得到的各分量信号作为行向量构造矩阵,最后对该矩阵进行以高斯性最大为度量的分离，逐一得到各激励源信号，并对其快速傅里叶变换得到频谱，结合机床结构及介观尺度下高速微铣削加工特点，识别微铣削力信号及机床状态信息。实验结果分析表明：该方法可成功对介观尺度下高速微铣削加工中主要激励源和机床状态进行有效识别。
Abstract
An effective separation algorithm is proposed for three orthogonal high speed micro milling force mixed-signal under mesoscale. Thus the real high speed micro milling force signals and machine vibration signals are obtained. Based on above algorithm, machine status is analyzed. The information content is designed as processing rule of initial separation and the mixed-signals are separated preliminarily. The component signals are obtained. As row vectors, the component signals are used to build the matrix. Finally, this matrix is separated with the rule of most Gaussian. The exciting source signals are separated one by one. The results are processed by FFT and the spectrums are obtained. Combining with the features of high speed micro milling machining under mesoscale, micro milling force signals and state information of machine are identified. The experiment results show that the algorithm can successfully indentify the main exciting sources and machine status in mesoscale high speed micro milling.
关键词
高速微铣削 /
介观尺度 /
矩阵构造 /
信号分离 /
机床状态
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Key words
high speed micro milling /
meso scale /
matrix creation /
signal separation /
machine status
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参考文献
[1] K. ZHU, G. S. HONG，Y. S. WONG，et al. Cutting force denoising in micro-milling[J].International Journal of Production Research, Vol. 46, No. 16, 15 August 2008, 4391–4408.
[2] Hua Shao, Xinhua Shi, Lin Li. Power signal separation in milling process based on wavelet transform and independent component analysis[J]. International Journal of Machine Tools & Manufacture , 2011, 51: 701–710.
[3] Pöyhönen S, Jover P, Hyötyniemi H. Independent component analysis of vibrations for fault diagnosis of an induction motor[C]. International Conference Circuits, Signals and Systems, Mexico. 2003, 1: 203-208.
[4] Lin J, Zhang A. Fault feature separation using wavelet-ICA filter[J]. NDT & E International, 2005, 38(6): 421-427.
[5] Gelle G, Colas M, Serviere C. Blind source separation: a tool for rotating machine monitoring by vibrations analysis[J]. Journal of sound and vibration, 2001, 248(5): 865-885.
[6] 李欣，梅德庆，陈子辰. 基于ICA的镗削过程颤振征兆信号分离方法研究[J]. 振动与冲击, 2013,32(9):5-9.
LI Xin, MEI De-qing, CHEN Zi-chen. ICA based separation of chatter symptom signals for precision hole boring processing[J]. Journal of Vibration and Shock, 2013,32(9):5-9.
[7] 胥永刚，张发启，何正嘉. 独立分量分析及其在故障诊断中的应用[J]. 振动与冲击, 2004, 23(2): 104-107.
XU Yong-gang, ZHANG Fa-qi, HE Zheng-jia. Independent component analysis and its applications to fault diagnosis [J]. Journal of Vibration and Shock, 2004, 23(2): 104- 107.
[8] 杨杰，郑海起，田昊，等. 基于独立分量分析的欠定盲源分离方法[J]. 振动与冲击, 2013, 32(7): 30-33.
YANG Jie, ZHENG Hai-qi, TIAN Hao, et al. Underdetermined bind source separation method based on independent component analysis[J]. Journal of Vibration and Shock, 2013, 32(7): 30-33.
[9] 赵学智，叶邦彦，陈统坚.奇异值差分谱理论及其在车床主轴箱故障诊断中的应用[J]. 机械工程学报，2010, 46（1）：100-108.
ZHAO Xuezhi YE Bangyan CHEN Tongjian. Difference Spectrum Theory of Singular Value and Its Application to the Fault Diagnosis of Headstock of Lathe [J]. Journal of Mechanical Engineering, 2010, 46（1）：100-108.
[10] 赵学智，陈统坚，叶邦彦. 基于奇异值分解的铣削力信号处理与铣床状态信息分离[J]. 机械工程学报, 2007, 43(6): 169-174.
ZHAO Xue-zhi, CHEN Tong-jian, YE Bang-yan. Processing of milling force signaland isolation of state information of milling machine based on singular value decomposition[J]. Chinses Journal of Mechanical Engineering, 2007, 43(6): 169-174.
[11] 李晓舟，于化东，许金凯，等. 微切削加工中切削力的理论与实验[J]. 光学精密工程, 2009,17(5): 1086-1092.
LI Xiao-zhou,YU Hua-dong,XU Jin-kai,et al. Theory and experiments of cutting forces in micro-cutting process[J]. Optics and Precision Engineering, 2009,17(5): 1086-1092.
[12] Vakondios D, Kyratsis P, Yaldiz S, et al. Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6[J]. Measurement, 45 (2012):1480-1488.
[13] Yue Sun, Yongsheng Liu, Zhanjiang Yu, et al. Milling Force Mixed-signal Denoising Based on ICA in High Speed Micro-milling[C]. IEEE International Conference on Robotics and Biomimetics (ROBIO), Guangzhou, China: IEEE. 2012: 1023-1028.
[14] 李一全，孙岳，董山恒，等. 基于独立分量分析的高速微铣削力混合信号噪声分离方法[J]. 中国测试，2013,39（2）：6-13.
LI Yi-quan, SUN Yue, DONG Shan-heng, et al. Milling Force Mixed-signal Denoising Based on ICA in High Speed micro-milling[J]. China Measurement & Test, 2013,39（2）：6-13.
[15] 李成峰，来新民，李洪涛，等. 介观尺度铣削工艺分析[J]. 农业机械学报, 2008, 39(1): 156-164.
LI Cheng-feng, LAI Xin-min, LI Hong-tao, et al. Technology Analysis on Mesoscale Milling[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018,39（1）：156-164.
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