Fast 2D crack profile reconstruction by image processing for Eddy-Current Testing (original) (raw)
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IEEE Transactions on Instrumentation and Measurement, 2008
Nondestructive testing techniques for the diagnosis of defects in solid materials can follow three steps, i.e., detection, location, and characterization. The solutions currently on the market allow for good detection and location of defects, but their characterization in terms of the exact determination of defect shape and dimensions is still an open question. This paper proposes a method for the reliable estimation of crack shape and dimensions in conductive materials using a suitable nondestructive instrument based on the eddy current principle and machine learning system postprocessing. After the design and tuning stages, a performance comparison between the two machine learning systems [artificial neural network (ANN) and support vector machine (SVM)] was carried out. An experimental validation carried out on a number of specimens with different known cracks confirmed the suitability of the proposed approach for defect characterization. Index Terms-Artificial neural network (ANN), eddy current testing (ECT), nondestructive testing (NDT), signal processing, support vector machine (SVM). I. INTRODUCTION N ONDESTRUCTIVE testing (NDT) is a very broad interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost-effective way. As requested by product norms [1], NDT technicians and engineers define and implement tests that locate and characterize material conditions and flaws. These tests are performed in a manner that does not affect the future usefulness of the object or material. Common technologies for NDT equipment include radiography or X-ray analysis, laser holography or gaging, penetrant testing, magnetic particle testing, and eddy current testing (ECT). Recent advances have made ECT more powerful, versatile, and useful for quality assurance. In fact, modern ECT techniques offer unique low-cost methods for the high-speed largescale inspection of metallic materials, such as those used in nuclear, aerospace, and maritime applications, where premature failures could give rise to economic problems and/or endanger human life [2]-[5]. The NDT eddy current (EC) tests can be divided into three steps, i.e., detection, location, and characterization. Detection is the ability to find the presence of a defect; location finds a specimen area where the defect is situated; and characterization Manuscript
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AIP Conference Proceedings, 2003
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One of the still open problems in the inspection research concerns the determination of the maximum depth to which a surface defect goes. Eddy current testing being one of the most sensitive well established inspection methods, able to detect and characterize different type of defects in conductive materials, is an adequate technique to solve this problem. This paper reports a study concerning the disturbances in the magnetic field and in the lines of current due to a machined linear defect having different depths in order to extract relevant information that allows the determination of the defect characteristics. The image of the eddy currents (EC) is paramount to understand the physical phenomena involved. The EC images for this study are generated using a commercial finite element model (FLUX). The excitation used produces a uniform magnetic field on the plate under test in the absence of defects and the disturbances due to the defects are compared with those obtained from experimental measurements. In order to increase the limited penetration depth of the method giant magnetoresistors (GMR) are used to lower the working frequency. The geometry of the excitation planar coil produces a uniform magnetic field on an area of around the GMR sensor, inducing a uniform eddy current distribution on the plate. In the presence of defects in the material surface, the lines of currents inside the material are deviated from their uniform direction and the magnetic field produced by these currents is sensed by the GMR sensor. Besides the theoretical study of the electromagnetic system, the paper describes the experiments that have been carried out to support the theory and conclusions are drawn for cracks having different depths.
Russian Journal of Nondestructive Testing, 2020
The study of 3D eddy current non destructive testing system for cracks characterization using finite element method requires a great amount of computing time and memory space. In this article, we have validated the developed model and then determined directly the crack length by analyzing the complete signal. Afterwards, we have extracted from the complete sensor sweep signal the maximal amplitude that we have exploited to estimate the crack depth.
Investigation of the magnetic field response from eddy current inspection of defects
The International Journal of Advanced Manufacturing Technology, 2011
Eddy current testing is one of the most widely used methods in non-destructive testing for the inspection of conductive materials. Numerical modelling of eddy current testing has emerged as an important approach alongside experimental studies. This paper investigates an application of numerical modelling and experimental study as a means of the quantitative non-destructive evaluation (QNDE) of defects in conductive samples. There are two methods of measuring eddy current response, more commonly by measuring the change in impedance of the eddy current probe coil, or as used in this work, by measuring the change in magnetic field directly using magnetic field sensors such as superconducting quantum interference devices, giant magneto resistance, or as in this case Hall sensors. Specifically, measurements made using an eddy current probe containing an excitation coil and a Hall sensor, experimentally obtained using an X-Y scanner table, are compared with a numerical (finite element method) model. The discrepancies between the experimen-tal tests and the numerical models have been analysed and explained, which is an important factor in engineering applications of QNDE.