Effecten van ruis in het meten van oppervlakken

Accurate measurement of areal surface topography is essential in precision engineering and manufacturing, where microscopic surface structures influence functional properties such as friction, wear, and sealing performance. However, surface measurements are often affected by measurement noise, instrument-specific artefacts, and limitations in current characterization methods defined in international standards. Research in this field therefore focuses on improving noise reduction techniques, understanding instrument behaviour, and strengthening the metrological robustness of surface parameters such as flatness deviation within the ISO 25178 framework. A novel noise reduction approach based on generalized Fourier decomposition has been extended from profile measurements to full surface datasets using Legendre polynomial expansions. This method enables the reconstruction of unbiased surface topographies from a limited number of noisy measurements, overcoming the limitations of conventional averaging techniques that require many repetitions and remain sensitive to form deviations such as sphericity or cylindricity. Validation using synthetic data and experimental measurements demonstrates effective noise reduction while preserving relevant surface features. Comparative analysis of multiple surface topography instruments further shows that noise estimates strongly depend on measurement parameters including sampling density, acquisition time, and internal data processing routines. These dependencies complicate direct comparisons between instruments and highlight the limitations of single-value noise metrics. Detailed reporting of measurement conditions is therefore essential for reliable instrument benchmarking and uncertainty evaluation. Fundamental assumptions, particularly that measurement noise is stationary and random, are also critically examined. Allan deviation analysis applied in both temporal and spatial domains reveals long-term drift, non-random fluctuations, and implicit spatial filtering introduced by instrument software. These effects can significantly influence measurement stability and apparent surface resolution. Allan deviation therefore provides a powerful diagnostic tool for identifying hidden instability sources and assessing measurement system limits. Application of these concepts in an international interlaboratory comparison evaluating flatness deviation according to ISO 25178-700 demonstrates both the potential and practical challenges of the emerging standard. Complementary surface parameters improve interpretability under realistic measurement conditions, while careful calibration, alignment, and data preprocessing are necessary to ensure consistent results across laboratories. Together, these developments contribute to more reliable and comparable surface measurements in high-precision industrial and scientific applications.