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Pod of ultrasonic detection of synthetic hard alpha inclusions in titanium aircraft engine forgings

机译:超声检测钛飞机发动机锻件中合成硬质α夹杂物的荚

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摘要

The probability of detection (POD) of inspection techniques is a key input to estimating the lives of structural components such as aircraft engines. This paper describes work conducted as a part of the development of POD curves for the ultrasonic detection of synthetic hard alpha (SHA) inclusions in titanium aircraft engine forgings. The sample upon which the POD curves are to be based contains four types of right circular SHAs that have been embedded in a representative titanium forging, as well as a number of flat bottomed holes (FBHs). The SHAs were of two sizes, #3 and #5, with each size including seeds with nominal nitrogen concentrations of both 3 and 17 wt. %. The FBHs included sizes of #1, #3, and #5. This discreteness of the data poses a number of challenges to standard processes for determining POD. For example, at each concentration of nitrogen, there are only two sizes, with 10 inspection opportunities each. Fully empirical, standard methodologies such as versus a provide less than an ideal framework for such an analysis. For example, there is no way to describe the beam limiting effect whereby the signal no longer increases the flaw grows larger than the beam, one can only determine POD at the two concentration levels present in the block, and confidence bounds tend to be broad because of the limited data available for each case. In this paper, we will describe strategies involving the use of physics‐based models to overcome these difficulties by allowing the data from all reflectors to be analyzed by a single statistical model. Included will be a discussion of the development of the physics‐based model, its comparison to the experimental data (obtained at multiple sites with multiple operators) and its implications regarding the statistical analysis, whose details will be given in a separate article by Li et al. in this volume.
机译:检查技术的检测概率(POD)是估算飞机发动机等结构部件寿命的关键输入。本文介绍了作为POD曲线开发工作的一部分,以超声检测钛飞机发动机锻件中合成硬α(SHA)夹杂物的方法。 POD曲线所基于的样本包含已嵌入代表性的钛锻件中的四种直角SHA,以及许多平底孔(FBH)。 SHA具有#3和#5两种尺寸,每种尺寸都包括标称氮浓度均为3和17 wt%的种子。 %。 FBH包括#1,#3和#5的大小。数据的这种离散性对确定POD的标准流程提出了许多挑战。例如,在每种氮浓度下,只有两种尺寸,每种都有10个检查机会。完全经验性的标准方法(例如vs)提供的分析方法并不理想。例如,没有办法描述光束限制效应,即信号不再增加缺陷,变得比光束更大,只能确定块中存在的两个浓度水平的POD,并且置信范围趋于宽泛,因为每个案例可用的有限数据中的多少。在本文中,我们将描述通过使用基于物理学的模型来克服这些困难的策略,该方法将允许通过单个统计模型分析来自所有反射器的数据。其中将讨论基于物理学的模型的开发,与实验数据的比较(在多个站点由多个操作员获得)及其对统计分析的影响,有关详细信息将在Li et al的另一篇文章中给出。等在这个卷中。

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