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Automated Wall Thickness Evaluation for Turbine Blades Using Robot-Guided Ultrasonic Array Imaging
(2024)
Nondestructive testing has become an essential part of the maintenance of modern gas turbine blades and vanes since it provides an increase in both safety against critical failure and efficiency of operation. Targeted repairs of the blade’s airfoil require localized wall thickness information. This information, however, is hard to obtain by nondestructive testing due to the complex shapes of surfaces, cavities, and material characteristics. To address this problem, we introduce an automated nondestructive testing system that scans the part using an immersed ultrasonic array probe guided by a robot arm. For imaging, we adopt a two-step, surface-adaptive Total Focusing Method (TFM) approach.
For each test position, the TFM allows us to identify the outer surface, followed by calculating an adaptive image of the interior of the part, where the inner surface’s position and shape are obtained. To handle the large volumes of data, the surface features are automatically extracted from the TFM images using specialized image processing algorithms. Subsequently, the collection of 2D extracted surface data is merged and smoothed in 3D space to form the outer and inner surfaces, facilitating wall thickness evaluation. With this approach, representative zones on two gas turbine vanes were tested, and the reconstructed wall thickness values were evaluated via comparison with reference data from an optical scan. For the test zones on two turbine vanes, average errors ranging from 0.05 mm to 0.1 mm were identified, with a standard deviation of 0.06–0.16 mm.
Quantification of impact damages in CFRP and GFRP structures with thermography and ultrasonics
(2018)
The extent of damage caused by impacts in fibre reinforced composites depends on the energy of the impacts, on the velocity and the shape of the impacting body, on the material and structure of the composite and on the geometry of the structure. Here, mainly the thickness of the component is essential. The non-destructive evaluation of these damages can be carried out using both ultrasound and active thermography methods. A comparison of the detection sensitivity of these methods for the different damages is carried out in this paper depending on the fibre composite material used (CFRP and GFRP), the thickness of the material and the impact energy. The NDT methods used after the damage are supplemented by thermographic measurements with high temporal resolution, which were already recorded during the impact.
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.
Defects in FRP structures may be introduced during the processing and fabrication of composite components and can initiate or grow in-service. In the context of this JRP, the term ‚defect‘ refers to imperfections introduced during manufacture/processing and/or secondary machining operations, as well as damage sustained during a component‘s service life. One of the challenges facing accurate and repeatable defect detection in FRP composites is the multitude of defect types that exist, each with characteristics that present different challenges to the NDE practitioner. In order for a particular NDE technique to achieve broad acceptance by industry, it is desirable for the technique to be able to detect a range of defect types with a high level of confidence. Project VITCEA (Validated Inspection Techniques for Composites in Energy Applications) is developing and validating traceable procedures for novel NDE techniques with contrasting detection capabilities, which will underpin the increased use of FRP composites for improved efficiency and reliability in energy related applications e.g. wind and marine turbine blades, nacelles, oil and gas flexible risers.
Reliability analysis of the ultrasonic inspection system for the inspection of hollow railway axles
(2016)
Axles are safety critical train components that are subjected to significant cyclic loading during operation. If the crack is initiated in the axle, cyclic loading will lead to crack propagation. To maintain structural integrity, axles must be periodically inspected for fatigue cracks in the material. Deutsche Bahn uses mechanized ultrasonic inspection system to inspect hollow railway axles. The inspections are performed from the bore surface, using several conventional transducers with different incident angles, inspecting the axle along the entire length. As with the every safety critical system, the reliability of these inspections must be determined with regard to their flaw detection capabilities. Traditionally this is done according to the relevant standards for railway vehicles. To investigate the capability of the NDT system more thoroughly, we want to evaluate the capability of the inspection system to detect flaws by means of probability of detection (POD) curves. It will be shown that other parameters, beside the size of the crack, for example crack position in the axle, influence the detection of the crack. The influence of these parameters was evaluated using ultrasonic simulation. The evaluation served as an input for the manufacturing of the flaws in the real scale axle. Once these axles are inspected and the data evaluated, using data from both measurement and simulation, we will express the POD of the crack as function of influencing parameters using the multiparameter POD model.