Cements and concretes materials characterisation using machine-learning-based reconstruction and 3D quantitative mineralogy via X-ray microscopy
Cements and concretes materials characterisation using machine-learning-based reconstruction and 3D quantitative mineralogy via X-ray microscopy
复制标题
通过 X 射线显微镜使用基于机器学习的重建和 3D 定量矿物学来表征水泥和混凝土材料
DOI：
10.1111/jmi.13278
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发表时间：
2024
影响因子：
2
通讯作者：
Mitchell R
中科院分区：
文献类型：
--
作者：
Mitchell R
X射线显微镜(XRM)的3D成像是断层成像的一种形式，它正在给材料表征带来革命性的变化。对不同尺度的颗粒、颗粒、界面和气孔进行无损成像分类，对于我们了解建筑材料的组成、结构和破坏是必不可少的。现在存在着各种工作流程，以最大限度地提高数据收集的效率，并突破以往所取得的成就，无论是从单一仪器、软件还是通过多模式相关显微镜的组合。XRM数据采集和数据处理工作流程是一个正在发展的感兴趣的领域；尤其重要的是改进对成像具有挑战性的样品的数据采集过程，这通常是因为它们的大小、密度(原子序数)和/或需要成像的分辨率。现代的进展包括深度/机器学习和人工智能解决方案，解决了数据重建过程中的伪影检测问题，提供了高级去噪，改进了特征量化，提高了数据/图像的比例，并提高了吞吐量，目标是在后处理期间增强分割和可视化，从而更好地表征样本。在这里，我们将三种基于人工智能和机器学习的重建方法应用到水泥和混凝土中，以帮助图像改善、更快的样本吞吐量、数据的放大和3D中的定量相识别。我们发现，通过应用先进的机器学习重建方法，有可能(I)通过使用DeepRecon Pro增强对比度和去噪，极大地改善水泥/混凝土“厚”岩心的扫描质量并增加吞吐量，(Ii)使用DeepScout将更高级别的数据扩展到更大的视野，以及(Iii)使用定量自动化矿物学来在3D中对矿物/物相组分进行空间表征和量化。这些方法显著提高了收集的XRM数据的质量，解决了以前无法访问的特征，并简化了扫描和重建过程，以获得更大的吞吐量。
3D imaging via X‐ray microscopy (XRM), a form of tomography, is revolutionising materials characterisation. Nondestructive imaging to classify grains, particles, interfaces and pores at various scales is imperative for our understanding of the composition, structure, and failure of building materials. Various workflows now exist to maximise data collection and to push the boundaries of what has been achieved before, either from singular instruments, software or combinations through multimodal correlative microscopy. An evolving area on interest is the XRM data acquisition and data processing workflow; of particular importance is the improvement of the data acquisition process of samples that are challenging to image, usually because of their size, density (atomic number) and/or the resolution they need to be imaged at. Modern advances include deep/machine learning and AI resolutions for this problem, which address artefact detection during data reconstruction, provide advanced denoising, improved quantification of features, upscaling of data/images, and increased throughput, with the goal to enhance segmentation and visualisation during postprocessing leading to better characterisation of samples. Here, we apply three AI and machine‐learning‐based reconstruction approaches to cements and concretes to assist with image improvement, faster throughput of samples, upscaling of data, and quantitative phase identification in 3D. We show that by applying advanced machine learning reconstruction approaches, it is possible to (i) vastly improve the scan quality and increase throughput of ‘thick’ cores of cements/concretes through enhanced contrast and denoising using DeepRecon Pro, (ii) upscale data to larger fields of view using DeepScout and (iii) use quantitative automated mineralogy to spatially characterise and quantify the mineralogical/phase components in 3D using Mineralogic 3D. These approaches significantly improve the quality of collected XRM data, resolve features not previously accessible, and streamline scanning and reconstruction processes for greater throughput.