An adaptive digital filter method for measuring the radial error motion of ultra-precision spindle

Abstract

To ensure the nanometer-level running accuracy of the ultra-precision spindle, the radial error motion should be precisely measured. However, the asynchronous errors induced by the measurement instrument will mix in the measured data and significantly deteriorate the measurement precision. In this study, a novel adaptive digital filter method (ADFM) is proposed to suppress the asynchronous errors and lower requirements for the precision of measurement instruments based on the Donaldson reversal method. Specifically, the Gaussian mixture model is first used to investigate the characteristics of asynchronous errors and obtain appropriate initial filter parameters. The least mean square algorithm is utilized to adaptively optimize the filter parameters. Monte Carlo simulation results indicate that ADFM can suppress asynchronous errors by more than 90% compared with the traditional methods. Experimental results show that the standard deviation uncertainty is reduced by more than 60%. Besides, the measurement results of two sensors with different precision verify the feasibility of ADFM.