2005Journal of Physics Conference SeriesOpen access

Accurate roughness measurements by dynamic calibration, VFM-uncertainty calculations and a special calibration specimen

Han Haitjema, M. Morel

Open full text 5 citations

Abstract

The uncertainty of roughness measurements and calibration is usually rather high. This paper gives some methods to estimate the uncertainty properly and proposes a dynamic calibration device and a special reference specimen to enable more accurate roughness measurements.

Open-access reader

About this research paper

What this paper is about

The uncertainty of roughness measurements and calibration is usually rather high. This paper gives some methods to estimate the uncertainty properly and proposes a dynamic calibration device and a special reference specimen to enable more accurate roughness measurements.

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The uncertainty of roughness measurements and calibration is usually rather high. This paper gives some methods to estimate the uncertainty properly and proposes a dynamic calibration device and a special reference specimen to enable more accurate roughness measurements.

Key concepts: Calibration, Surface finish, Measurement uncertainty, Surface roughness, Computer science, Remote sensing, Materials science, Mathematics

Related papers

Back to paper searchBrowse research topicsOriginal source
Accurate roughness measurements by dynamic calibration, VFM-uncertainty calculations and a special calibration specimen — Research Paper | ScholarLens