基于振源识别的高速微铣削机床状态研究

发布时间：2018-05-02 07:17

本文选题：高速微铣削 + 介观尺度　；参考：《振动与冲击》2015年09期

【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。
[Abstract]:This paper presents an effective separation algorithm for the mixed signals of high speed micro-milling force in mesoscopic scale, and obtains the real signal of the high speed micro-milling force and the vibration signal of the machine tool. Based on this, the state of the machine tool is analyzed. In the algorithm, the observed signal is initially separated according to the amount of information, and then the separated component signal is taken as the line vector construction matrix. Finally, the matrix is separated with Gao Si maximum as the metric, and each excitation source signal is obtained one by one. The frequency spectrum is obtained by fast Fourier transform, and the signal of micro-milling force and the state information of machine tool are identified according to the structure of machine tool and the characteristics of high-speed micro-milling in mesoscopic scale. The experimental results show that this method can effectively identify the main excitation sources and machine tool states in mesoscopic scale high speed micro-milling.
【作者单位】：长春理工大学机电工程学院;
【基金】：国家自然科学基金(51275056)
【分类号】：TG54

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