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Surface roughness characterization using representative elementary area (REA) analysis.

Kuldeep Singh1, Nitin Paliwal2, Konstantinos Kasamias2

  • 1Department of Earth Sciences, Kent State University, 325 S. Lincoln St., Kent, OH, 44242, USA. ckuldeep@kent.edu.

Scientific Reports
|January 20, 2024
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Summary
This summary is machine-generated.

The Representative Elementary Area (REA) method ensures accurate surface roughness measurements. This analysis reveals how roughness parameters like mean height (Sa) stabilize with increasing investigation area, reducing uncertainty in surface characterization.

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Area of Science:

  • Surface Metrology
  • Materials Science
  • Confocal Laser Scanning Microscopy

Background:

  • Surface roughness characterization is crucial for material performance.
  • Traditional methods may not capture multiscale roughness effectively.
  • Evaluating representative roughness parameters requires careful consideration of the analysis area.

Purpose of the Study:

  • To introduce and validate the Representative Elementary Area (REA) analysis method.
  • To investigate the convergence of surface roughness parameters with increasing Area of Investigation (AOI).
  • To establish relationships between polishing parameters, material properties, and surface roughness metrics.

Main Methods:

  • Confocal Laser Scanning Microscopy was used to acquire surface topography data.
  • Representative Elementary Area (REA) analysis was applied to combined scan tiles.
  • Mean height (Sa), skewness, kurtosis, and autocorrelation length (Sal) were analyzed across varying AOIs and polishing grit sizes (#60-#1200).

Main Results:

  • Surface roughness parameters, including Sa, exhibited convergence to a steady state as AOI increased.
  • The steady-state Sa followed an inverse power law with polishing grit size, dependent on material hardness.
  • Autocorrelation length (Sal) showed a complex relationship with AOI, with a maximum value related to REA.

Conclusions:

  • The REA analysis method is essential for obtaining reliable and representative surface roughness parameters.
  • Ignoring REA analysis leads to significant uncertainty in roughness measurements, especially for coarser polishing.
  • The study demonstrates the multiscale nature of surface texture and its convergence behavior.