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MObjects--A Novel Method for the Visualization and Interactive Exploration of Defects in Industrial XCT Data

机译:MObjects-工业XCT数据缺陷可视化和交互式探索的新方法

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This paper describes an advanced visualization method for the analysis of defects in industrial 3D X-Ray Computed Tomography (XCT) data. We present a novel way to explore a high number of individual objects in a dataset, e.g., pores, inclusions, particles, fibers, and cracks demonstrated on the special application area of pore extraction in carbon fiber reinforced polymers (CFRP). After calculating the individual object properties volume, dimensions and shape factors, all objects are clustered into a mean object (MObject). The resulting MObject parameter space can be explored interactively. To do so, we introduce the visualization of mean object sets (MObject Sets) in a radial and a parallel arrangement. Each MObject may be split up into sub-classes by selecting a specific property, e.g., volume or shape factor, and the desired number of classes. Applying this interactive selection iteratively leads to the intended classifications and visualizations of MObjects along the selected analysis path. Hereby the given different scaling factors of the MObjects down the analysis path are visualized through a visual linking approach. Furthermore the representative MObjects are exported as volumetric datasets to serve as input for successive calculations and simulations. In the field of porosity determination in CFRP non-destructive testing practitioners use representative MObjects to improve ultrasonic calibration curves. Representative pores also serve as input for heat conduction simulations in active thermography. For a fast overview of the pore properties in a dataset we propose a local MObjects visualization in combination with a color-coded homogeneity visualization of cells. The advantages of our novel approach are demonstrated using real world CFRP specimens. The results were evaluated through a questionnaire in order to determine the practicality of the MObjects visualization as a supportive tool for domain specialists.
机译:本文介绍了一种先进的可视化方法,用于分析工业3D X射线计算机断层扫描(XCT)数据中的缺陷。我们提出了一种新颖的方法来探索数据集中的大量单个对象,例如,在碳纤维增强聚合物(CFRP)的孔提取的特殊应用领域中展示的孔,夹杂物,颗粒,纤维和裂缝。计算完各个对象属性的体积,尺寸和形状因子后,所有对象都将聚类为一个平均对象(MObject)。可以交互地浏览生成的MObject参数空间。为此,我们介绍了呈放射状和平行排列的均值对象集(MObject Sets)的可视化。通过选择特定的属性(例如,体积或形状因数)以及所需的类数,可以将每个MObject分为多个子类。迭代地应用此交互式选择会导致沿所选分析路径对MObject进行预期的分类和可视化。因此,通过可视链接方法可以可视化分析路径下方MObject的给定不同缩放比例。此外,将代表性的MObject作为体积数据集导出,以用作后续计算和模拟的输入。在CFRP中确定孔隙率的领域中,无损检测从业人员使用代表性的MObjects来改善超声校准曲线。代表性的孔也可以用作主动热成像中导热模拟的输入。为了快速浏览数据集中的孔隙特性,我们提出了结合颜色编码的细胞均匀性可视化效果的局部MObjects可视化效果。我们的新方法的优势已通过实际的CFRP标本得以证明。通过问卷对结果进行了评估,以确定MObjects可视化作为领域专家支持工具的实用性。

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