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Related Experiment Video

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Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography
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Using Wavelet Packet Transform for Surface Roughness Evaluation and Texture Extraction.

Xiao Wang1, Tielin Shi2, Guanglan Liao3

  • 1State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430000, China. wangxiao1989@hust.edu.cn.

Sensors (Basel, Switzerland)
|April 27, 2017
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Summary

Wavelet packet transform precisely characterizes surface roughness and extracts texture from engineering surfaces. This method accurately quantifies surface properties, aiding in manufacturing analysis and feature recognition.

Keywords:
roughness analysissurface characterizationwavelet packet transform

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Area of Science:

  • Materials Science
  • Metrology
  • Signal Processing

Background:

  • Surface characterization is crucial for evaluating functional performance.
  • Traditional methods may have limitations in precisely separating roughness and texture.
  • Advanced signal processing techniques offer potential for improved surface analysis.

Purpose of the Study:

  • To introduce and validate the use of wavelet packet transform for surface roughness characterization and texture extraction.
  • To compare wavelet packet transform results with traditional measurement methods.
  • To demonstrate the applicability of wavelet packet transform on real engineering surfaces.

Main Methods:

  • Surface topography acquisition using confocal laser scanning microscopy.
  • Application of smooth border padding and de-noising for precise roughness surface generation.
  • Utilizing wavelet packet transform to analyze high-frequency components for texture separation and extraction.
  • Calculation and comparison of profile and areal roughness parameters.

Main Results:

  • Wavelet packet transform accurately reconstructs surface roughness and waviness.
  • Extracted textures effectively reflect surface structure and manufacturing conditions.
  • Most calculated roughness parameters show good agreement with profile meter measurements.
  • Simulations helped explain deviations in parameters like skewness due to noise.

Conclusions:

  • Wavelet packet transform is a robust tool for comprehensive surface characterization.
  • The method enables precise evaluation of surface roughness and effective extraction of surface texture.
  • This approach enhances feature recognition and matching for engineering surfaces.