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NLPAR: Non-local smoothing for enhanced EBSD pattern indexing.

Patrick T Brewick1, Stuart I Wright2, David J Rowenhorst1

  • 1The U. S. Naval Research Laboratory, Materials Science and Technology Division, Washington, DC, 20375, USA.

Ultramicroscopy
|March 3, 2019
PubMed
Summary
This summary is machine-generated.

A new non-local smoothing kernel enhances electron backscattered diffraction (EBSD) pattern re-indexing. This method achieves higher index success rates than NPAR and rivals dictionary-based approaches with less computational cost.

Keywords:
EBSDKikuchi patternsNon-local means filteringPattern indexing

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Area of Science:

  • Materials Science
  • Crystallography
  • Microscopy

Background:

  • Electron Backscattered Diffraction (EBSD) is a key technique for microstructural analysis.
  • Advances in SEM technology and computational processing enable complex EBSD data analysis.
  • Accurate indexing of diffraction patterns is crucial for reliable microstructural characterization.

Purpose of the Study:

  • To introduce a novel post-processing technique for enhancing EBSD patterns.
  • To improve the re-indexing success rate of EBSD data.
  • To offer a computationally efficient alternative to existing indexing methods.

Main Methods:

  • Development of a non-local smoothing kernel algorithm (NLPAR) for EBSD pattern enhancement.
  • Weighting of the smoothing kernel based on the exponential decay of Euclidean distance between patterns.
  • Comparison of NLPAR performance against Neighbor Pattern Averaging with Re-indexing (NPAR) and dictionary-based approaches using Inconel 600.

Main Results:

  • The proposed non-local smoothing kernel approach consistently yields a higher Index Success Rate (ISR) compared to NPAR.
  • NLPAR achieves ISRs comparable to dictionary-based methods.
  • The algorithm operates efficiently, suitable for large smoothing kernels without compromising boundary integrity.

Conclusions:

  • The NLPAR algorithm offers a significant improvement in EBSD data re-indexing accuracy.
  • This method provides a computationally efficient and effective alternative for microstructural analysis.
  • The developed algorithm enhances the utility of EBSD for materials characterization.