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Deconvoluting thermomechanical effects in X-ray diffraction data using machine learning.

Rachel E Lim¹, Shun Li Shang¹, Chihpin Chuang²

¹Pennsylvania State University, University Park, PA 16802, USA.

Acta Crystallographica. Section A, Foundations and Advances

|January 31, 2025

Summary

This study introduces a new method combining physics-based modeling and machine learning to separate thermal and mechanical strains in X-ray diffraction data. This approach enhances understanding of material behavior under thermomechanical loading, particularly during laser melting.

Keywords:

Gaussian process regression first-principles calculations machine learning physics-based modeling strain stress superalloys synchrotron X-ray diffraction

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Area of Science:

Materials Science
Computational Materials Science
Crystallography

Background:

X-ray diffraction (XRD) is crucial for analyzing crystalline materials under thermomechanical loading.
Spatial broadening and overlapping diffraction peaks complicate the analysis of complex strain states.
Existing methods struggle to deconvolve distinct lattice deformation mechanisms.

Purpose of the Study:

To develop a novel approach for deconvoluting thermal and mechanical elastic strains from XRD data.
To analyze the thermomechanical state evolution during laser melting of Inconel 625.
To improve the accuracy of strain analysis in crystalline materials.

Main Methods:

Utilized a combination of physics-based modeling (heat transfer, fluid flow, elasto-plasticity, XRD simulations) and machine learning (Gaussian Process Regression).
Generated training data using simulations to map diffracted intensity to strain fields.
Employed first-principles density functional theory for accurate material property calculations.

Main Results:

Successfully deconvoluted thermal and mechanical elastic strains from complex XRD patterns.
Extracted the evolution of the thermomechanical state during laser melting of Inconel 625.
Demonstrated the capability of trained models to analyze irregularly shaped diffraction peaks.

Conclusions:

The developed method effectively separates thermal and mechanical strain contributions.
This approach provides detailed insights into the thermomechanical behavior of materials during rapid processing.
The machine learning-enhanced XRD analysis offers a powerful tool for materials characterization.