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A lightweight transformer for faster and robust EBSD data collection.

Harry Dong1, Sean Donegan2, Megna Shah2

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, 15289, USA. harryd@andrew.cmu.edu.

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|December 1, 2023
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Summary
This summary is machine-generated.

This study introduces a novel transformer-based method to recover missing data slices in 3D electron back-scattered diffraction (EBSD) microscopy. The approach enhances data quality and collection speed for materials science applications.

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Area of Science:

  • Materials Science
  • Data Science
  • Microscopy

Background:

  • Three dimensional electron back-scattered diffraction (3D EBSD) microscopy is vital in materials science.
  • Data quality in 3D EBSD can be compromised during serial-sectioning collection.
  • The sequential nature of 3D EBSD data lends itself to deep learning approaches.

Purpose of the Study:

  • To develop a robust method for recovering missing slices in 3D EBSD volumes.
  • To accelerate 3D EBSD data collection and improve overall data quality.
  • To overcome challenges of deep learning with high-dimensional, scarce data.

Main Methods:

  • A two-step method combining an efficient transformer model and a projection algorithm.
  • Self-supervised learning using synthetically generated 3D EBSD data.
  • Training a deep learning model to process sequential 3D EBSD data.

Main Results:

  • Superior recovery accuracy on real 3D EBSD data compared to existing methods.
  • Demonstrated effectiveness of the transformer model in processing sequential data.
  • Successful training of a deep learning model using only synthetic data.

Conclusions:

  • The proposed transformer-based method effectively recovers missing slices in 3D EBSD data.
  • This approach enhances the robustness and efficiency of 3D EBSD data collection.
  • Self-supervised learning with synthetic data is a viable strategy for training deep learning models in this domain.