利用机器学习解卷积X射线衍射数据中的热机械效应
Deconvoluting Thermomechanical Effects in X-ray Diffraction Data using Machine Learning
AI总结：
本文提出融合物理建模与GPR机器学习的X射线衍射分析方法，实现热应变与机械应变的解卷积及应变分布提取。
摘要：
X射线衍射非常适合在晶体材料承受复杂或快速热机械载荷期间探测其亚表面状态。然而，随着衍射体积尺寸增大，空间展宽以及无法解卷积不同晶格变形机制的影响带来了挑战。在此，我们提出一种结合基于物理的建模与机器学习的新方法，用于在衍射数据分析中解卷积热弹性应变与机械弹性应变。该方法建立在此前从衍射数据中提取热应变分布信息的工作基础上。新方法被应用于提取Inconel 625壁试样激光熔化过程中热机械状态的演化，该过程在冷却时会产生显著的残余应力。研究结合传热与流体流动、弹塑性以及X射线衍射模拟，为机器学习（高斯过程回归，GPR）模型生成训练数据，这些模型将衍射强度分布映射到潜在的热机械应变场。第一性原理密度泛函理论用于确定准确的、随温度变化的热膨胀和弹性刚度，并将其用于弹塑性建模。结果发现，经过训练的GPR模型即使面对具有不规则形状峰的复杂衍射图谱，也能够解卷积热应变和机械应变的影响，此外还能提供有关潜在应变分布的信息。
英文摘要：
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.
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