Chatter vibration deteriorates the machining accuracy and shortens the tool life. Thus, In-process monitoring methods have been proposed by using additional sensors such as dynamometers and acceleration sensors. However, introducing additional sensors leads to high cost and reduction of machine-tool stiffness. To solve these problems, a sensor-less monitoring method applying the disturbance observer theory was proposed in our previous research which has experimentally shown that chatter vibration can be detected only from the servo information in milling. For further demand, because chatter vibration can be classified into self-excited vibration and forced vibration, an assorted detection method is required. In this study, we propose a novel frequency analysis method combining moving variance and moving Fourier transform algorithms, which can obtain the power spectrum density of each chatter vibration type separately with small computational load. The validity of the proposed method is evaluated through milling tests with different rotational speeds.
|ジャーナル||Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering|
|出版ステータス||Published - 2015 7月 1|
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