从合金元素的重量分数预测AA2011铝合金的Hounsfield单位(HU): x射线计算机断层扫描研究

A. Baydoun, R. Hamade
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引用次数: 0

摘要

x射线计算机断层扫描越来越多地被用作检测内部缺陷和材料识别的无损检测方法。然而,由于金属及其合金的高HU值,以及x射线扫描参数的可能组合范围很广,许多金属和合金的平均Hounsfield单位(HU)测量在文献中没有报道。因此,开发一种方法,可以准确地预测材料的平均Hounsfield单位值,给定其元素组成和其组成元素的平均Hounsfield测量值,这是有价值的,可以帮助填补报告不足的材料的空白。本研究首先基于混合模型,采用两种方法预测AA2011年的平均Hounsfield值。其次,研究了x射线扫描参数对AA2011平均Hounsfield单位值预测精度的影响。为此,x射线CT扫描在三个x射线管电流水平(50、100和200 mAs)和两个管电压水平(120和140kVp)下进行。还考虑了扫描窗口大小(扫描视图场)的影响,以体素大小表示,其中使用了三种尺寸(0.00287,0.0176和0.0452 mm3)。样品厚度的影响通过三个厚度水平(1.5、3和6毫米)进行评估。结果表明,两种方法均具有较好的预测能力,而第二种方法的预测精度更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the Hounsfield Unit (HU) of Aluminum Alloy AA2011 From the Weight Fractions of its Alloying Elements: An X-Ray Computed Tomography Study
X-ray computed tomography scans are being increasingly employed as a non-destructive testing method for the detection of internal defects and material identification. However, mean Hounsfield Unit (HU) measurements for many metals and alloys are unreported in the literature due to the high HU values of metals and their alloys and also due to the wide range of possible combinations of X-ray scanning parameters. For this reason, developing a method that can accurately predict the mean Hounsfield unit value of a material given its elemental composition and mean Hounsfield measurements of its constituent elements is valuable and can help fill the gap for under-reported materials. In this study, first, the mean Hounsfield values for AA2011 are predicted using two methods based on the mixture model. Second, the effect of the X-ray scanning parameters on the predictive accuracy of the mean Hounsfield Unit value for AA2011 is studied. To this end, X-ray CT scans are performed at three X-ray tube current levels (50, 100, and 200 mAs) and two tube voltage levels (120 and 140kVp). Also considered is the effect of scan window size (field of scan view) as represented by voxel size where three sizes (0.00287, 0.0176, and 0.0452 mm3) are utilized. The effect of sample thickness is assessed via three thickness levels (1.5, 3, and 6 mm). The findings show that both methods show good predictive ability with the second method showing greater accuracy.
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