Bai Long, Velichko Alexander, Drinkwater Bruce W
IEEE Trans Ultrason Ferroelectr Freq Control. 2019 Nov;66(11):1798-1813. doi: 10.1109/TUFFC.2019.2927439. Epub 2019 Jul 8.
In the field of ultrasonic array imaging for non-destructive testing (NDT), material structural noise caused by grain scattering is one of the main sources of error when characterizing defects that are found in the polycrystalline materials. The existence of grains can also severely affect the detection performance of ultrasonic testing, making small defects indistinguishable from the grain indications due to ultrasonic attenuation and backscatter. This paper proposes a model in which the statistical distribution of the defect data is obtained from different realizations of the grain structure. This statistical distribution, termed the defect+grains model in this paper, is shown to contain information that is needed for detection and characterization of defects. Hence, given a specific measurement configuration, the characterization result can be obtained by constructing a defect+grains model based on the multiple realizations of each possible defect and calculating their probability. The detection, classification, and sizing accuracy are shown to be predictable by quantifying the probabilities that an experimentally measured defect matches the different defect+grains models. This defect+grains modeling approach gives insight into the detection/characterization problem, leading to an evaluation of the fundamental limits of the achievable inspection performance.
在用于无损检测（NDT）的超声阵列成像领域，晶粒散射引起的材料结构噪声是表征多晶材料中发现的缺陷时的主要误差来源之一。晶粒的存在还会严重影响超声检测的性能，由于超声衰减和反向散射，使得小缺陷与晶粒信号难以区分。本文提出了一个模型，其中缺陷数据的统计分布是从晶粒结构的不同实现中获得的。这种统计分布在本文中称为缺陷+晶粒模型，它被证明包含了缺陷检测和表征所需的信息。因此，给定特定的测量配置，可以通过基于每个可能缺陷的多个实现构建缺陷+晶粒模型并计算其概率来获得表征结果。通过量化实验测量缺陷与不同缺陷+晶粒模型匹配的概率，检测、分类和尺寸测量精度被证明是可预测的。这种缺陷+晶粒建模方法深入了解了检测/表征问题，从而对可实现的检测性能的基本极限进行了评估。
