Introduction and comparison of new EBSD post-processing methodologies.

Area of Science:

• Materials Science
• Crystallography
• Electron Microscopy

Background:

• Electron Backscatter Diffraction (EBSD) is crucial for microstructure characterization.
• Obtaining indexable diffraction patterns can be challenging, leading to noisy EBSD maps.
• Existing post-processing methods correlate neighboring points to improve data quality.

Purpose of the Study:

• To introduce and evaluate two novel post-processing techniques for improving noisy EBSD data.
• To assess the effectiveness of these methods on artificially and operationally induced noise, as well as challenging sample types.
• To validate indexing success rate metrics for EBSD data analysis.

Main Methods:

• Development of a re-scanning approach utilizing local pattern averaging.
• Implementation of a method using multiple solutions from the triplet indexing technique.
• Application of these methods to EBSD data with introduced noise and to deformed/fine-grained samples.

Main Results:

• Both introduced techniques improved the quality of the reconstructed EBSD scan data.
• Local pattern averaging demonstrated the most significant improvement among the tested methods.
• Local pattern averaging was most effective for noise originating from camera operating conditions.

Conclusions:

• The novel post-processing techniques effectively enhance noisy EBSD datasets.
• Local pattern averaging is a promising method, especially for camera-related noise.
• The fraction of points with high Confidence Index (CI) values accurately reflects EBSD indexing success rates.