Reference object for evaluating the accuracy of porosity measurements by X-ray computed tomography

Petr Hermanek, Simone Carmignato
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引用次数: 33

Abstract

Internal defects such as voids and porosity directly influence mechanical properties, durability, service life and other characteristics of industrial parts. There are several non-destructive and destructive methods for defects detection and evaluation. Recently, X-ray Computed Tomography (CT) has emerged as an effective tool for geometrical characterization of internal defects. 3D information about internal voids/porosity extracted from CT datasets can be utilized in many applications, such as production processes optimization and quality control. However, there are still challenges in using CT as a traceable method for internal voids dimensional measurements. In order to enhance the accuracy and reliability of CT porosity measurements, a metrological validation method is required.

This study presents the application of a new reference object for accuracy evaluation of CT porosity measurements and discusses results obtained by using it. The reference object is made of aluminium and is composed of a cylindrical body and four cylindrical inserts with micro-milled hemispherical features of calibrated sizes resembling artificial flaws. The accuracy of porosity measurements is evaluated according to various characteristics (diameters and depths measurements errors) and repeatability of measurements. Design of experiments technique is used to investigate the influence of CT parameters settings on porosity measurement accuracy.

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评价x射线计算机断层扫描孔隙度测量精度的参考对象
空隙、孔隙等内部缺陷直接影响工业零件的机械性能、耐久性、使用寿命等特性。有几种无损和破坏性的缺陷检测和评估方法。近年来,x射线计算机断层扫描(CT)已成为内部缺陷几何表征的有效工具。从CT数据集中提取的有关内部孔隙度的3D信息可用于许多应用,例如生产过程优化和质量控制。然而,将CT作为一种可追溯的方法用于内部空隙尺寸测量仍然存在挑战。为了提高CT孔隙度测量的准确性和可靠性,需要一种计量验证方法。本文介绍了一种新的参考对象在CT孔隙度测量精度评价中的应用,并讨论了使用它获得的结果。参考物体由铝制成,由一个圆柱形体和四个圆柱形嵌件组成,这些圆柱形嵌件具有校准尺寸的微磨半球形特征,类似于人工缺陷。孔隙度测量的准确性是根据各种特征(直径和深度测量误差)和测量的可重复性来评估的。采用实验设计技术研究了CT参数设置对孔隙度测量精度的影响。
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Contents Editorial Board Editorial Board Contents Comparison of surface-based and image-based quality metrics for the analysis of dimensional computed tomography data
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