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High-throughput determination of grain size distributions by EBSD with low-discrepancy sampling
Timothy J H Long1, William Holbrook2, Todd C Hufnagel1,2
1Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, Maryland, USA.
Journal of Microscopy
|November 22, 2023
Summary
Accelerating microstructure analysis using Electron Backscatter Diffraction (EBSD) is crucial for materials science. This study introduces an iterative sampling method that significantly reduces scan points needed for accurate grain size distribution measurements.
Area of Science:
- Materials Science
- Crystallography
- Computational Materials Science
Background:
- Microstructure significantly influences the mechanical properties of structural materials.
- Rapid quantification of microstructures is essential for high-throughput materials screening.
- Electron Backscatter Diffraction (EBSD) is a key technique for microstructure analysis but can be time-consuming.
Purpose of the Study:
- To develop and validate an iterative method for reducing EBSD scan points.
- To accelerate the measurement of grain size distributions (GSDs) from EBSD data.
- To enable faster high-throughput screening of structural materials.
Main Methods:
- Implemented an iterative approach using incremental low-discrepancy sampling (Sobol and R2 sequences).
- Incorporated on-the-fly GSD calculations and a Kolmogorov-Smirnov test for convergence.
- Tested the method on five EBSD maps from magnesium AZ31B specimens, comparing R2, Sobol, and random sampling.
Main Results:
- The R2 sampling sequence achieved statistically similar GSDs to full-density scans using only 52% of the data on average.
- For monodisperse GSDs with over 1000 grains, R2 sampling required just 39% of the EBSD points.
- R2 sampling demonstrated superior efficiency compared to Sobol and random sampling methods.
Conclusions:
- The proposed iterative low-discrepancy sampling method effectively reduces EBSD scan points for accurate GSD determination.
- R2 sampling offers a significant acceleration for EBSD-based microstructure analysis, particularly for high-throughput applications.
- This approach enhances the efficiency of materials characterization without compromising data quality.
