Deconvoluting Thermomechanical Effects in X-ray Diffraction Data using Machine Learning

TL;DR

This paper introduces a machine learning approach combining physics-based models to deconvolute thermal and mechanical strains from X-ray diffraction data during rapid thermomechanical processes, enabling detailed sub-surface state analysis.

Contribution

It presents a novel method integrating physics simulations and Gaussian Process Regression to separate thermal and elastic strains in diffraction data, improving analysis of complex thermomechanical states.

Findings

GPR models accurately deconvolute thermal and mechanical strains

Method effectively analyzes complex diffraction patterns with irregular peaks

Application to laser melting of Inconel 625 demonstrates practical utility

Abstract

X-ray diffraction is ideal for probing sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach to use combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis. The method builds on a previous effort to extract thermal strain distribution information from diffraction data. The new approach is applied to extract the evolution of thermomechanical state during laser melting of an Inconel 625 wall specimen which produces significant residual stress upon cooling. A combination of heat transfer and fluid flow, elasto-plasticity, and X-ray diffraction simulations are used to generate training data for machine-learning (Gaussian Process Regression, GPR) models that map diffracted intensity distributions to underlying thermomechanical strain fields. First-principles density functional theory is used to determine accurate temperature-dependent thermal expansion and elastic stiffness used for elasto-plasticity modeling. The trained GPR models are found to be capable of deconvoluting the effects of thermal and mechanical strains, in addition to providing information about underlying strain distributions, even from complex diffraction patterns with irregularly shaped peaks.