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Related Experiment Video

Updated: Jan 10, 2026

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
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Surface roughness profile separation using singular spectrum analysis.

Ziming Pang1,2, Xiaochuan Gan2, Ming Kong1

  • 1College of Metrology, Testing and Instrumentation, China Jiliang University, Hangzhou, China.

Plos One
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Singular Spectrum Analysis (SSA) effectively separates surface roughness profiles, offering results comparable to the Gaussian filter for key parameters like Ra and Rq. This makes SSA a promising alternative for surface metrology applications.

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Area of Science:

  • Metrology
  • Mechanical Engineering
  • Signal Processing

Background:

  • Surface roughness is crucial for assessing mechanical component quality.
  • Accurate separation of roughness profiles is essential for metrology.

Purpose of the Study:

  • To evaluate Singular Spectrum Analysis (SSA) for surface roughness profile separation.
  • To compare SSA's effectiveness against the ISO standard Gaussian filter.
  • To investigate the impact of SSA parameters (window length, grouping) on roughness metrics.

Main Methods:

  • Utilized NIST roughness measurement data.
  • Applied Singular Spectrum Analysis (SSA) for profile separation.
  • Compared SSA results with the Gaussian filter for parameters such as Ra, Rq, and Rku.

Main Results:

  • SSA effectively separates surface roughness signals with appropriate window length.
  • SSA yields roughness parameter values comparable to the Gaussian filter.
  • Key roughness parameters (Ra, Rq, Rku) are accurately estimated using SSA.

Conclusions:

  • Singular Spectrum Analysis (SSA) is a viable alternative for surface roughness profile separation.
  • SSA demonstrates broad applicability in surface metrology.
  • The study validates SSA's capability to provide reliable surface roughness measurements.