On the Artifacts Involved in the Measurements of Engineering 3D Topography and a Correction Method

Surface roughness is a key tribological property commonly characterized by the power spectral density (PSD) of surface topography. However, the recent Surface Topography Challenge demonstrated that measurements of identical surfaces may yield PSD curves differing by several orders of magnitude depending on the laboratory and measurement method. Such discrepancies can arise from measurement artifacts, including spike-like outliers and macroscopic surface curvature. In this work, we analyze these effects and propose a correction procedure for recovering the intrinsic roughness spectrum. The method combines nonlinear median filtering for artifact detection with robust PSD reconstruction based on multiple one-dimensional surface sections. Outliers are removed in real space, the macroscopic shape is eliminated by detrending, and the PSD is obtained as the median of spectra from individual line scans. Tests on synthetic surfaces with known roughness spectra contaminated by curvature and artificial spikes demonstrate that the method reliably recovers the original spectrum even when artifacts dominate the raw data. Application to experimentally measured surfaces further indicates that some apparent roughness features may originate from measurement noise and stitching artifacts rather than the true surface structure.