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Improved Methodology for Inspection Reliability Assessment for Detecting Synthetic Hard Alpha Inclusions in Titanium

机译:改进的检测可靠性评估方法,用于检测钛中的合成硬α夹杂物

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摘要

This paper presents a progress report on the development of model-based methodology to estimate Nondestructive Evaluation (NDE) capability. Previous work on this project was reported in Meeker et al. [3, 4]. The methodology uses combinations of physical modeling of an inspection process, along with laboratory and production data, to estimate Nondestructive Evaluation (NDE) capability. The methodology is based on a physical/statistical prediction model and will be used to predict NDE capability in terms of Probability of Detection (POD), Probability of False Alarm (PFA), Probability of Indication (POI) and Receiver Operating Characteristic (ROC) curves. The physical model explains and allow predictions for the effects of making changes to the inspection setup (e.g. transducer properties and scan increment). The statistical/empirical model will quantify unexplained variability, adjust for potential model bias, and provide a means for obtaining corresponding uncertainty intervals. The work has been motivated by the need for methods to predict ultrasonic (UT) inspection POD for detecting hard-alpha and other subsurface flaws in titanium using gated peak detection. This is a challenging problem because the inspection must detect very complex subsurface flaws in the presence of significant “material” noise. The underlying framework of the methodology should, however, be general enough to apply to other NDE methods. This paper describes recent work based on application of the new methodology to the detection of synthetic hard alpha flaws in titanium alloys. In particular we describe and illustrate methods to assess the effect that changes in scan plans and gate width will have on POD.
机译:本文提出了有关基于模型的方法来评估无损评估(NDE)能力的进展报告。 Meeker等人报道了该项目的先前工作。 [3,4]。该方法结合了检查过程的物理建模以及实验室和生产数据的组合,以评估无损评估(NDE)能力。该方法基于物理/统计预测模型,将用于根据检测概率(POD),虚警概率(PFA),指示概率(POI)和接收器工作特征(ROC)来预测NDE能力。曲线。物理模型可以解释并允许对更改检查设置的影响进行预测(例如,换能器属性和扫描增量)。统计/经验模型将量化无法解释的可变性,调整潜在的模型偏差,并提供一种获取相应不确定性区间的方法。这项工作的需要是需要一种方法来预测超声(UT)检测POD,以使用门控峰检测来检测钛中的硬α和其他表面缺陷。这是一个具有挑战性的问题,因为在存在明显的“材料”噪声的情况下,检查必须检测出非常复杂的表面缺陷。但是,该方法的基本框架应具有足够的通用性,以适用于其他NDE方法。本文介绍了基于新方法在钛合金中合成硬α缺陷的检测中的最新工作。特别是,我们描述和说明了评估扫描计划和栅极宽度变化对POD的影响的方法。

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