A wavelet method based on goodness of fit test for roughness of micro-EDM

The surface topography errors of micro electrical discharge machining (Micro-EDM) are mainly composed of surface roughness, surface waveness and profile error, which have influence on the workpiece´s functions and performances in various degrees. Thus, how to pick up these errors without distortion is quite important for evaluating the surface topography. Further research shows that the frequency of surface roughness, surface waveness and profile error are different. Surface roughness belongs to high frequency, comparatively, surface waveness and profile error belong to low frequency. It is well known that Wavelet Transform has good time-frequency localization properties, which is especially suitable for signals De-noising. Therefore, in this paper, a wavelet method for roughness of Micro-EDM´s surface contour lines is presented, in which, it is demonstrated that multiresolution analysis is feasible for Micro-EDM´s surface roughness separation, and the time of wavelet decomposition is determined by Pearson Chi-square goodness of fit test. Furthermore, the results of simulation and experiments show that the proposed methods can separate the roughness of Micro-EDM and improve some arbitrary defects that the time of wavelet decomposition is determined by the cutoff wavelength and sampling interval, meanwhile, using the mean and variance of ideal surface as accuracy requirements of the reference line data could help the separation have a certain certainty and precision.