基于振源识别的高速微铣削机床状态研究

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摘要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。

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	孙岳
	于占江
	许金凯
	于化东

关键词 ：高速微铣削, 介观尺度, 矩阵构造, 信号分离, 机床状态

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.

Key words： high speed micro milling meso scale matrix creation signal separation machine status

收稿日期: 2013-05-03 出版日期: 2015-05-15

引用本文:

孙岳，于占江，许金凯，于化东. 基于振源识别的高速微铣削机床状态研究[J]. 振动与冲击, 2015, 34(9): 65-70.
Yue Sun, Zhanjiang Yu, Jinkai Xu, Huadong Yu . Study of High Speed Micro Milling Machine Status Based on Vibration Source Identification. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(9): 65-70.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2015/V34/I9/65

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