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Estimating microstructural feature distributions from image data using a Bayesian framework.

Noah Wade1, Lori Graham-Brady1

  • 1Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, Maryland, USA.

Journal of Microscopy
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

Quantifying measurement error in pixelized microstructural data is crucial. This study introduces a Bayesian method to estimate uncertainty, finding international standards may be overly conservative for grain size measurements.

Keywords:
grain size distributionsmeasurement errorsampling errorsampling resolutionuncertainty quantification

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Metrology

Background:

  • Microstructural characterization often uses pixelized grids, introducing measurement error proportional to resolution.
  • Quantification of this error is typically not performed, impacting data reliability.
  • International standards offer minimum sampling recommendations but lack error quantification.

Purpose of the Study:

  • To develop a novel method for quantifying relative uncertainty in pixelized microstructural measurements.
  • To assess the impact of sampling resolution on geometric property measurements.
  • To evaluate the adequacy of current international standards for grain size measurement.

Main Methods:

  • A Bayesian framework was employed to compute the distribution of true geometric properties.
  • Simulated data collection was performed on features from a Voronoi tessellation.
  • The method was applied to measure size, aspect ratio, and perimeter of microstructural components.

Main Results:

  • A conditional feature distribution was computed, providing quantitative estimates of relative uncertainty.
  • Size distributions demonstrated the least sensitivity to sampling resolution.
  • Evidence suggests international standards are overly conservative for grain size in Voronoi tessellations.

Conclusions:

  • The developed Bayesian approach effectively quantifies uncertainty in pixelized microstructural data.
  • Sampling resolution significantly influences measurements of aspect ratio and perimeter.
  • Current international standards for grain size measurement may require revision based on microstructure type.