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Nondestructive testing (NDT) is routinely used in the nuclear, rail, aerospace and automotive industries to search for flaws in components. A signal from the flaw, recorded by the NDT device, will vary from measurement to measurement. The sources of this innate signal variation can be categorized into intrinsic, human and application factors. This variation, especially when searching for flaws that are at the limits of the NDT detection capabilities, can result in a failure to detect a flaw. If the inspected components are safety critical, the capability of NDT system to find flaws must be determined in order to avoid the catastrophic consequences of a missed flaw. The NDT system capability to detect flaws is expressed in terms of reliability. The probability of detection (POD) curve is a widespread tool to quantify the reliability of NDT. The POD is determined by series of experiments on specimens containing a range of flaws with known characteristics. The production of a sufficient number of these flaws is time consuming and expensive. In this paper, a multi-parameter POD model that uses both simulation and experimental measurements to calculate the POD curves will be presented. Simulation is used to assess the intrinsic capability of the NDT system and the variability in the system is estimated from experimental measurements. The POD calculated with the multi-parameter model is more comprehensive than the one calculated with the traditional model and the number of costly experiments needed is reduced.
Role of materials data
(2017)
In a general sense, data are the output of experimental research work. In this workshop we will present some basic ideas concerning the reliability of experimental data; this is a central issue both for individuals or labs producing results as well as for scientists working on theoretical models and engineers designing a machine. Digitalization accelerates enormously the data exchange between these communities; however, the required level of confidence on experimental results increases in the same way. We invite all the participants to reflect upon the value of materials data for their own work.
Today, it is an established fact that the capability of the non-destructive testing (NDT) to find flaws can be properly addressed only in terms of probability of detection (POD). The probabilistic, signal-response model, introduced in 1980s, was developed with experimental observation of eddy-current inspections of flat plate samples, containing surface breaking cracks. A linearity between the peak voltage measured by the testing system, and the crack depth was observed. The influence of the crack depth was therefore seen as the major influencing factor for the POD, whereas other factors merely caused the variability in the measurement. This model has proven itself valid for those inspection cases where there is only one main influencing factor on the POD (usually the flaw size) and other factors have a lesser influence. But with increasing requirements to quantify the capability of NDT systems in complex inspection situations, where several factors have a major influence on the POD, it has become clear that the applicability of this simple model has reached its limits. In disregard to its limitations, this model is regularly applied to those situations in which its fundamental assumptions are invalid, forcing evaluators and NDT researchers into attempts to fit the data to an unsuitable model, instead of fitting an appropriate model to the data.
The multiparameter POD model, developed in the early 2010s, enables more factors that influence detection to be simultaneously analysed, making the POD a function of multiple factors. The model is based on the inspection’s physical model, to describe the influence of different factors on the response signal. Measurement variability is obtained from the experiment. Using this model assisted determination of the POD, the necessary number of flaws for evaluation is reduced and a more comprehensive understanding of the inspection is obtained. Several examples of the successful application of the multi-parameter POD model in different fields will be presented.