In the world of computational materials science, the knowledge of microstructure is vital in understanding the process-microstructure–property linkage across various length-scales. To circumvent costly experimental characterizations, typically, analyses on ensembles of 3D microstructures within a numerical framework are preferred. Utilizing a moment invariants-based physical descriptor, the current work quantifies the variations in the microstructural topology of 3D synthetic data of polycrystalline materials. For the first time, the validation of synthetic microstructures based on two unique AI-based reconstruction approaches was compared, providing valuable insights into the diverse characteristics of each methodology. Virtual 3D microstructure volumes of forged Ti-7Al and additively manufactured 316L stainless steel alloys were generated from 2D experimental data using two methods — Markov Random Field (MRF) and deep learning-based volumetric texture synthesis. Quantitative evaluation and validation of the reconstructed volumes were carried out with the aid of moment invariants by comparing local features associated with grain-level properties, such as grain size and shape. The normalized central moments previously employed to compare 2D grain topology were expanded to 3D. With the advent of various reconstruction algorithms, especially AI-based, the validation methodology outlined in this work can be adopted to evaluate the robustness of various 3D reconstruction frameworks as well as ensure spatial equivalency of the target microstructures.

中文翻译：

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随机微观结构表征和重建的比较和验证：机器学习与深度学习方法


在计算材料科学领域，微观结构知识对于理解不同长度尺度的过程-微观结构-性能联系至关重要。为了避免昂贵的实验表征，通常首选在数值框架内对 3D 微观结构的整体进行分析。目前的工作利用基于矩不变量的物理描述符，量化了多晶材料 3D 合成数据的微观结构拓扑的变化。首次对基于两种独特的基于人工智能的重建方法的合成微观结构的验证进行了比较，为每种方法的不同特征提供了宝贵的见解。锻造 Ti-7Al 和增材制造的 316L 不锈钢合金的虚拟 3D 微观结构体积是使用两种方法从 2D 实验数据生成的：马尔可夫随机场 (MRF) 和基于深度学习的体积纹理合成。通过比较与晶粒级特性（例如晶粒尺寸和形状）相关的局部特征，借助矩不变量对重建体积进行定量评估和验证。先前用于比较 2D 晶粒拓扑的归一化中心矩已扩展到 3D。随着各种重建算法（尤其是基于人工智能的重建算法）的出现，可以采用本文概述的验证方法来评估各种 3D 重建框架的鲁棒性，并确保目标微观结构的空间等效性。