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Related Concept Videos

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Updated: Oct 23, 2025

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
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Intragranular strain estimation in far-field scanning X-ray diffraction using a Gaussian process.

Axel Henningsson1, Johannes Hendriks2

  • 1Division of Solid Mechanics, Lund University, Lund, Sweden.

Journal of Applied Crystallography
|August 25, 2021
PubMed
Summary
This summary is machine-generated.

A new Gaussian process regression method enhances intragranular strain field estimation in polycrystalline materials using scanning 3DXRD data. This robust method offers improved accuracy and uncertainty quantification for materials science research.

Keywords:
Gaussian processesintragranular strainscanning X-ray diffractionthree-dimensional X-ray diffraction (3DXRD)

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Crystallography

Background:

  • Accurate intragranular strain field estimation is crucial for understanding polycrystalline material behavior.
  • Existing scanning 3D X-ray diffraction (3DXRD) methods have limitations in robustness and uncertainty quantification.
  • Linear and angular momentum balance are fundamental principles in mechanical analysis.

Purpose of the Study:

  • To develop and evaluate a novel regression method for intragranular strain field estimation using scanning 3DXRD data.
  • To incorporate a priori anisotropic compliance and enforce momentum balance for improved strain reconstruction.
  • To provide spatial uncertainty estimates and optimize spatial smoothness constraints.

Main Methods:

  • Utilized Gaussian process (GP) regression for strain field reconstruction.
  • Enforced linear and angular momentum balance using known anisotropic compliance.
  • Optimized spatial smoothness constraints via hyperparameter estimation.
  • Validated the method using synthetic data and experimental data from a tin (Sn) specimen.

Main Results:

  • The GP regression method demonstrated superior performance compared to an algebraic inversion technique.
  • Achieved lower root-mean-square errors, mean absolute errors, and maximum absolute errors across all strain components.
  • Successfully provided spatial estimates of uncertainty in the reconstructed strain fields.
  • The method showed robustness in reconstructing strain fields from both synthetic and experimental data.

Conclusions:

  • The proposed Gaussian process regression method offers a more robust and accurate approach for intragranular strain field estimation in polycrystalline materials.
  • This method addresses key limitations of previous scanning 3DXRD reconstruction techniques.
  • The ability to quantify strain uncertainty and optimize spatial smoothness enhances the reliability of the results for materials characterization.