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Phase Mapping in EBSD Using Convolutional Neural Networks.

Kevin Kaufmann¹, Chaoyi Zhu², Alexander S Rosengarten¹

¹Department of NanoEngineering, UC San Diego, La Jolla, CA92093, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|May 12, 2020

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Machine learning now enables high-throughput material phase mapping using electron backscatter diffraction (EBSD) pattern analysis. This automated approach accurately separates phases by crystal symmetry, chemistry, and lattice parameters, reducing manual input.

Area of Science:

Materials Science
Crystallography
Computational Materials Science

Background:

Commercial electron backscatter diffraction (EBSD) equipment revolutionized materials characterization by providing orientation maps.
Advancements in EBSD technology have improved data quality, detection rates, and analysis capabilities.
Current EBSD methods often require significant human intervention for phase identification and mapping.

Purpose of the Study:

To demonstrate a novel, high-throughput methodology for material phase mapping using machine learning and EBSD.
To leverage diffraction pattern information directly for automated phase separation.
To address key challenges in modern EBSD analysis, including accuracy and efficiency.

Main Methods:

Collected diffraction patterns from diverse material samples.

Keywords:

EBSD convolutional neural network crystal structure electron diffraction machine learning

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Trained a convolutional neural network (CNN), a machine learning algorithm, to recognize subtle differences in diffraction patterns.

Utilized the trained CNN to autonomously output phase maps based on pattern recognition.

Main Results:

The machine learning approach accurately separated material phases based on crystal symmetry, chemistry, and lattice parameters.
The methodology demonstrated the capability to perform phase mapping with reduced human decision-making.
This study represents the first successful demonstration of machine learning coupled with EBSD for autonomous phase mapping.

Conclusions:

Machine learning techniques, specifically CNNs, can effectively interpret EBSD diffraction patterns for automated phase mapping.
This approach offers a significant advancement towards high-throughput materials characterization.
The developed methodology provides a scalable path for phase mapping as EBSD pattern databases grow.