Suppression of the High-Frequency Errors in Surface Topography Measurements Based on Comparison of Various Spline Filtering Methods

Przemysław Podulka

doi:10.3390/ma14175096

Abstract

The metrology of so-called “engineering surfaces” is burdened with a substantial risk of both measurement and data analysis errors. One of the most encouraging issues is the definition of frequency-defined measurement errors. This paper proposes a new method for the suppression and reduction of high-frequency measurement errors from the surface topography data. This technique is based on comparisons of alternative types of noise detection procedures with the examination of profile (2D) or surface (3D) details for both measured and modelled surface topography data. In this paper, the results of applying various spline filters used for suppressions of measurement noise were compared with regard to several kinds of surface textures. For the purpose of the article, the influence of proposed approaches on the values of surface topography parameters (from ISO 25178 for areal and ISO 4287 for profile standards) was also performed. The effect of the distribution of some features of surface texture on the results of suppressions of high-frequency measurement noise was also closely studied. Therefore, the surface topography analysis with Power Spectral Density, Autocorrelation Function, and novel approaches based on the spline modifications or studies of the shape of an Autocorrelation Function was presented.

Highlights

These include a classification of errors: Those caused by the environment [2], errors caused by the instrument such as internal noise and quantization errors or, errors caused by the data analysis, e.g., numerical uncertainties and model approximations
The primary objective of this paper is to propose and compare various procedures for the detection of high-frequency measurement noise from the surface topography data of the different types of surface textures
When the frame of the received function is accelerating near the maximum value (“1”) of the Autocorrelation Function (ACF), the High-Frequency Noise [13] (HFN) occurrence can be observed

Summary

Introduction

Introduction to the High-Frequency Errors in Surface Topography Analysis. It was assumed that many different factors could create noise affecting the uncertainty in the measurement of surface topography. These include a classification of errors: Those caused by the environment [2] (such as mechanical, acoustics vibrations, electromagnetic interference, etc.), errors caused by the instrument such as internal noise and quantization errors or, errors caused by the data analysis, e.g., numerical uncertainties and model approximations. The high-frequency noise can be caused by instability of the mechanics with any influences from the environment or by internal electrical noise. The high-frequency noise, in most cases, is the result of vibration

Objectives

Results

Conclusion