使用基于机器学习的重建和3D定量矿物学通过X射线显微镜对水泥和混凝土材料进行表征
Ria L Mitchell1, Andy Holwell1, Giacomo Torelli2
1Carl Zeiss Microscopy, ZEISS House, Cambridge, UK.
Journal of microscopy
|March 8, 2024
概括
人工智能和机器学习增强了材料科学的3DX射线显微镜 (XRM). 这些先进的技术提高了图像质量,使数据能够进行升级,并加速了对水泥和混凝土等具有挑战性的样品的表征.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算科学 计算科学
- 地质学和地质物理学
背景情况:
- 三维X射线显微镜 (XRM) 对于非破坏性材料的表征至关重要,它可以分析组成,结构和故障机制.
- 在XRM的挑战包括成像大,密集,或高分辨率的样本,影响数据采集和处理.
- 人工智能 (AI) 和机器学习 (ML) 的进步为XRM数据中的文物检测,无声化,量化和升级提供了解决方案.
研究的目的:
- 应用基于AI和ML的重建方法来改进XRM数据采集和处理水泥和混凝土样本.
- 为了证明增强的图像质量,更快的吞吐量,数据升级和定量阶段识别在3D中.
- 解决以前无法访问的特征,并简化对具有挑战性的材料的表征工作流.
主要方法:
- 应用了三种基于AI/ML的重建方法:DeepRecon Pro用于图像增强和无色化,DeepScout用于数据升级,Mineralogic 3D用于定量自动化矿物学.
- 使用XRM (断层扫描) 对水泥和混凝土样品进行非破坏性3D成像.
- 对AI/ML增强数据与传统方法进行比较分析,以评估质量,速度和分辨率的改进.
主要成果:
- DeepRecon Pro通过增强对比度和消除噪音,显著提高了厚/混凝土核心的扫描质量和吞吐量.
- DeepScout成功地升级了XRM数据,使得更大的视野可以被可视化.
- 矿物学3D提供了精确的3D空间表征和矿物学/相位元件的量化.
结论:
- 基于AI和ML的重建显著提高了XRM数据质量,并简化了对水泥和混凝土等具有挑战性的材料的处理.
- 这些先进的方法可以解决更细微的细节,并改善3D材料相的定量分析.
- 集成的工作流加速了样品吞吐量,并扩大了XRM在材料表征方面的功能.
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