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Non-Destructive Testing (NDT) is routinely used in aerospace, nuclear, railway and automotive industries. The most common use of NDT is to find flaws in components. In dynamically loaded components, flaws grow, and if they are not detected in time and no corrective measures are taken they can reach critical sizes. When a flaw reaches a critical size, it endangers the structural integrity of the component and will likely cause the component to fail. To detect flaws before they reach a critical size, components are inspected in fixed time intervals with non-destructive testing systems. In order to determine the amount of time between inspections, three variables are necessary: the critical size of the flaw, the flaw propagation speed and the size of the flaw that can be reliably detected by NDT. All three variables are, to a certain extent, stochastic. The critical size of the flaw and flaw propagation speed are questions of fracture mechanics. The size of the flaw that can be reliably detected by NDT is predicted by POD curves. The POD is determined for a given inspection scenario. The inspection scenario includes the NDT system, the component geometry and the type of the flaw. If one of these changes, the POD will also change and needs to be recalculated. As a result, the design of a dynamically loaded safety-relevant components is an iterative process in which a geometry is sought that provides an optimum between structural integrity and inspectability. A model that describes the iterative process for the design of the safe components with examples from praxis will be presented.
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.
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.