随动磨削曲轴表面异常轮廓误差去除方法
Removal Method Research on Following Ground Crankshaft Profile Error with Outliers

在高精度随动曲轴磨床中，为控制曲轴加工轮廓误差常采用预补偿的方法,如果测量时工件上有磨屑、毛刺等干扰将在测量结果中引入明显的异常轮廓，采用传统的高斯滤波器对数据进行处理将极大地影响轮廓误差的补偿精度,甚至导致废品出现。针对这一问题，给出了适用于闭轮廓的高斯滤波器、R-k滤波器和鲁棒高斯回归滤波器的理论模型。分别应用3种滤波器，对比分析结果可知，鲁棒高斯回归滤波器去除异常轮廓误差效果最理想，并通过人为改变异常轮廓的尺度，进一步验证鲁棒高斯回归滤波器的适应性和可靠性。该滤波方法集成到随动曲轴磨床软件中，实现异常轮廓的自动去除，提高了补偿效率，有效保证了曲轴磨削轮廓的误差精度。
In high precision crankshaft following grinder, pre-compensation method is usually used to control crankshaft profile error. Obvious outliers may appear in measured profile error data if there are burrs or scratches in the ground part surface. If traditional Gaussian filter is still applied to process these data, compensation precision of profile error can be impacted obviously, and even scraps may be generated. In order to solve existing problems, Gaussian filter, Rk filter and robust Gaussian regression filter are used for closed profile error. The three filters are applied to analyze measured data. Comparing the results analyzed, it is obvious that the robust Gaussian regression filter has the strongest outlier removal effect of profile error in three filters. By changing the scale of outlier profile error, robust Gaussian regression filter can be validated with high reliability and adaptability. This filter method is integrated in pin-chasing grinder software. Outliers can be removed automatically. Compensation efficiency and following ground crankshaft profile error accuracy can be increased.