DECREASING THE AMPLITUDE CHARACTERISTIC DEVIATION OF GAUSSIAN FILTER IN SURFACE ROUGHNESS MEASUREMENTS

Abstract

A new approach for decreasing the amplitude characteristic deviation of Gaussian filter in surface roughness measurements is presented. On the basis of the central limit theorem, various kinds of Gaussian approximation filters can be carried out. The cascaded first-order Butterworth filters and the cas- caded moving average filters are used respectively to implement the Gaussian filter approximately. Their amplitude characteristic deviation curves are almost equal in their shapes and opposite in their phases, and their locations of extremum are very close to each other. So the linear combination of the two Gaussian approximation filters may reduce the amplitude characteristic deviation greatly. The most amplitude deviation of a simple combination filter consisting of two approximation filters is about 0.11%. This new Gaussian approximation filter is both efficient and accurate for surface roughness measu-rements.