This paper presents an analytical continuous smoothing method for the five-axis toolpath by simultaneously scheduling the tool position and tool orientation trajectories. In order to ensure the high-order continuous, the peak-controlled jerk and arclength-parameterized property, a novel curve ''airthoid'' is proposed for the first time. The biairthoid is involved to smooth the corners of the tool position in the workpiece coordinate system (WCS) and the corners of the tool orientation in the machine coordinate system (MCS), the geometries of which are analytically determined by the user-defined deviation errors. A time synchronization strategy is proposed to extend the duration of the predetermined cubic acceleration profile to a specified time. With the kinematic constraints of the tool position and the tool orientation, the transitional and rotational trajectories are analytically synchronized by sharing the same motion time. To comply with the constraints of the linear feed drives, an optimization strategy is conducted by adjusting the kinematics of the tool position. By doing so, the approximation errors of the tool position and tool orientation in the WCS are strictly satisfied. The analytical arclength expression of the smoothing curves is more suitable for the on-line interpolation. Due to the arclength-parametrized transition curve, the feedrate fluctuation is eliminated. With the proposed time synchronization strategy, the physical limits of the feed drives are all respected. Moreover, the high-order continuous airthoid makes the motion more smoothing-going. Simulations and experiments verify the effectiveness of the proposed algorithm.
The internal sensor signals of the numerical controlled (NC) machine tools contain abundant information that associated with the operating state and the machining fault. However, the signal characteristics extracted in the time /frecquency domain miss its physical significance. This paper presents a signal preprocessing method in the spatial domain to extract the physical meaning characteristic of the internal sensor signals with the varying duty operation.The proposed method uses the encoder signal to resample the other condition signals in the spatial domain firstly. Then, the signals are analyzed by the Fourier transform to get the spectrum. Compared with the traditional methods, the physical meaning of the signal can be intuitively identified. Moreover, the characteristics can be obtained in the varying duty operation, instead of the uniform motion in the tradition methods. It is meaningful for the on-line monitoring, since the working condition of the machine tool is always changing in the machining process. The simulations and experiments verify the effectiveness of the proposed algorithm.
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