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Multi-probe method for straightness profile measurement based on least uncertainty propagation (1st report) Two-point method considering cross-axis translational motion and sensor's random error

机译:基于最小不确定度传播的直线度轮廓测量多探针方法(第一份报告)考虑到跨轴平移运动和传感器随机误差的两点方法

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摘要

In the straightness profile measurement of a mechanical workpiece, hardware datums have been the traditional standard. However, error separation techniques of the surface profile from parasitic motions have been developed. These are known as software datums, which separate the surface profile from the parasitic motions using multiple sensors and/or multiple orientations and realize higher accuracy than that of the hardware datum. However, the conventional software datum cannot measure a large-scale workpiece because the large sampling number causes random error amplification. Furthermore, the conventional software datum assumes that sensor's random noise is small enough in comparison with the parasitic motions. But, the accuracy of the hardware datum has become high. Then, the accuracy of the sensor's random noise is not so small, relatively. In this paper, a next-generation software datum, the two-point method based on the least uncertainty propagation, is proposed. The proposed two-point method consists of weighting and inverse filtering, resulting in the least uncertainty of the estimated surface profile by choosing suitable weighting.
机译:在机械工件的直线度测量中,硬件数据已成为传统标准。然而,已经开发了表面轮廓与寄生运动的误差分离技术。这些被称为软件基准,它使用多个传感器和/或多个方向将表面轮廓与寄生运动分开,并实现了比硬件基准更高的精度。但是,传统的软件数据无法测量大型工件,因为较大的采样数会导致随机误差放大。此外,常规软件数据假定传感器的随机噪声与寄生运动相比足够小。但是,硬件数据的准确性已经很高。这样,传感器的随机噪声的精度就不会那么低。本文提出了一种基于最小不确定性传播的下一代软件基准点两点法。所提出的两点方法由加权和逆滤波组成,通过选择合适的加权可以使估计的表面轮廓的不确定性最小。

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