NOISE CORRECTION FOR SURFACE MEASUREMENTS

In roughness measurements often noise plays a role. Noise may give an offset in measurement parameter as noise makes the parameter deviate away from zero. In this paper we propose a method to correct for noise bias for the surface parameter Sq. By considering the decrease in Sq once an average over multiple measurements is made, an unbiased value for Sq is estimated by extrapolating the value to an infinite amount of measurements. It is shown that using this method for two measurements only, the true measurand is approached better than with averaging tens of measurements. This principle is extended to obtain a complete ‘noise-corrected’ surface by considering the power spectrum and the change of each Fourier component with averaging. Combining the two methods and considering the statistical significance of each Fourier component enables a further reduction. Examples and simulations are shown for the calibration of roughness drive axis and surface measurements.