International Journal of Applied Electromagnetics and Mechanics

Several researches have confirmed the possibility of localizing small anomalies via Kirchhoff migration (KM); however, when the background information is unknown, small anomalies cannot be satisfactorily retrieved. This fact can be examined through the simulation results; however, related theoretical result to explain the reason of such phenomenon has not yet been investigated. In this contribution, we show that the imaging function of the KM can be expressed by an infinite series of the Bessel function of the first kind, material properties, and antenna arrangement, and applied alternative value of the background wavenumber. Based on the theoretical result, we explain why the exact location and shape of anomalies cannot be retrieved. The simulation results with synthetic data exhibited to support the theoretical result.

It is still a challenging problem to detect small defects for eddy current array probes, which requires the probes to possess excellent sensitivity, as well as high spatial resolution. This paper presents a novel high-resolution magnetic field imaging probe with two rows of tunneling magnetoresistance (TMR) array sensors. The bare die sensors are integrated on a printed circuit board by golden wire bonding technology. The two rows of sensors are placed staggered with each other. The data of the two arrays are merged into a matrix, in which way the image pixel pitch is increased to 0.25 mm. The probe employs a differential scheme to suppress the noise, so as to detect the weak signal of small defects. To highlight the weak defect indications, feature extraction and segmentation algorithms are developed. The experimental results confirm that the proposed method can inspect a small defect with dimensions 1 mm (length) × 0.1 mm (width) × 0.1 mm (depth) on a stainless-steel sample.

In this paper we present a first non-iterative imaging method for nonlinear materials, based on Monotonicity Principle. Specifically, we deal with the inverse obstacle problem, where the aim is to retrieve a nonlinear anomaly embedded in linear known background.

The Monotonicity Principle (MP) is a general property for various class of PDEs, that has recently generalized to nonlinear elliptic PDEs. Basically, it states a monotone relation between the point-wise value of the unknown material property and the boundary measurements. It is at the foundation of a class of non-iterative imaging methods, characterized by a very low execution time that makes them ideal candidates for real-time applications.

In this work, we develop an inversion method that overcomes some of the peculiar difficulties in practical application of MP to imaging of nonlinear materials, preserving the feasibility for real-time applications. For the sake of clarity, we focus on a specific application, i.e. the Magnetostatic Permeability Tomography where the goal is retrieving the unknown (nonlinear) permeability by boundary measurements in DC operations. This choice is motivated by applications in the inspection of boxes and containers for security.

Reconstructions from simulated data prove the effectiveness of the presented method.

Machining technology plays a major role in modern manufacturing industries due to the growing demand for equipment and products for aerospace, automotive, precision machinery sectors etc. A key issue associated with any type of machining operation is the accelerated tool wear which leads to shorter tool life, thus to low machining precision and decreased productivity. In this paper an electromagnetic Non-Destructive Testing method (NDT), and in particular Eddy Current Testing (ECT), is used to detect the wear of used cutting tools vs a new one.

A new approach for the solution of transient eddy-current problems involving pieces with one direction of symmetry is presented. The approach is applicable to pieces with translational or rotational symmetry. A Fourier series decomposition of the solution is introduced in the direction of symmetry, which converts the original 3D numerical problem into a series of independent 2D problems. The decomposition has significant benefits in terms of computational time and numerical noise reduction, and is inherently parallelisable.

The reliability of thimble tubes plays a critical role for maintaining the safety of a nuclear power plant. The defect depth needs to be quantified and predicted to support the operational decision-making. This paper presents a method to quantify the defects on thimble tube wall based on the analyzation of eddy current testing (ECT) data. Then, a method using artificial neural network (ANN) to predict the detect depth is studied. The tubes are divided into 2 shapes and four regions according to their positions and the data of each region and each shape is expanded by mean interpolation. A prediction model based on ANN is constructed for each shape in each region. The experimental results show that the model can predict the signal of the next year according to the signal of the previous three years with mean absolute percentage error less than 16%.

This paper introduces Buckingham’s 𝜋 theorem in the context of Non-Destructive Testing & Evaluation (NDT&E). Its application leads to easier problems to handle by reducing the number of variables involved. In this sense, dimensional analysis can provide the foundation for in-line, real-time and low-cost inspection methods that are fully compatible with the requirements of the Industry 4.0 and NDE 4.0 paradigms. In order to show the impact of the Buckingham’s 𝜋 theorem in NDT&E, we consider a practical case of interest, i.e. the simultaneous estimation of thickness and electrical conductivity of metallic plates via Eddy Current Testing. An initial numerical analysis is carried out with the aim to show the metrological performance of the method. The results obtained show that the method combines good accuracy with low computational costs.

Probability of Detection (PoD) models, in general, take into consideration one or multiple flaw parameters such as its length, maximum depth, and/or maximum surface area, and one flaw signal. However, due to correlation between the response signals, it might be necessary to consider multiple flaw response signals simultaneously. Hence, in this work, we demonstrate the possibility of including multiple correlated flaw signals, features, towards the construction of a PoD curve. The flaw features considered are the 3 components of the magnetic flux density. This is a simulation based PoD estimation for a narrow opening notch type flaw located in the sub-surface of a two-layer geometry. The inspection is carried out by an uniform eddy current probe that induces a spatially uniform fields into the conducting space, around the region of interest. The analysis was performed using the semi-analytical boundary element method (BEM).

With the development of non-destructive inspection techniques, more and more challenging situations have arisen and the correct choice of operating parameters can be decisive for a good detection sensitivity. Based on that, an algorithm was developed to obtain the best combination of an eddy current sensor operation parameters. The results obtained demonstrate the improvement in the cracks detection in welded parts after optimization.

This paper presents a portable device based on an Anisotropic Magnetoresistance (AMR) sensor for Steel Health Monitoring. The system operates by detecting magnetic anomalies in ferromagnetic materials caused by strain, corrosion, etc. This sensor can have various applications in the transportation, building, and aerospace fields for safety and maintenance monitoring of ferromagnetic materials. In this work, a low-cost device, that combines a high-sensitivity AMR sensor, a microcontroller, and supporting electronics has been designed and implemented. This sensor allows the contactless measurement of the magnetic flux density along three axes, when placed above the material under test, while the microcontroller and the required electronics enable real-time analysis and monitoring of measurements. In order to house and protect the sensor under various circumstances, a 3D-printed enclosure has also been created. This device can be used along with rehabilitation techniques for treatment of defective areas of an under-test material. Its versatility allows it to be employed in a variety of testing conditions for both single-point and scanning mode monitoring. The device’s portability, ease of use and applicability to on-site measurements make it accessible to a wide range of users, requiring only a personal computer to display the measurements. Finally, measurements are presented to prove the device’s accuracy for steel health monitoring.

Eddy current measurements are prone to edge effects when measurements are carried out near a sample boundary. In order to reduce these edge effects, typically ferrite shields are used which constrain the magnetic field within the shield volume. However, their effects on sensitivity towards changes in conductivity for Sweep Frequency Eddy Current (SFEC) measurements have not been investigated. A 2D axisymmetric Finite Element Model (FEM) has been developed to predict the frequency response of impedance of SFEC probes. The model predicted that the addition of shields minimized edge effect but with reduced sensitivity. A simulation study is carried out to optimize the probe design to increase the sensitivity of shielded ferrite probes. Based on these results, a probe is constructed with a custom ferrite shield and its shielding efficiency and probe sensitivity are compared experimentally with those for the probe with conventional ferrite shields.

HP austenitic stainless steel undergoes microstructural aging due to prolonged exposure to oxidizing and corrosive atmospheres in steam reforming furnaces. The derived aging states are classified by its service temperature and microstructural markers and monitoring it is important to residual life assessment. In this regard it was used a portable Eddy Current inspection system with the aid of machine learning classification tools, characterizing aging states in HP steel in real-time. The classification profile of a 12-meter tube was acquired, validated through Field Metallurgical Replication. The developed Eddy Current inspection system successfully differentiates three regions, revealing a progression of aging states.

Non-destructive thermographic testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers is a challenge, both because of its underlying complexity and because of the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. To provide a rigorous framework that accepts data from different modalities and allows data fusion as well, a Bayesian neural network (BNN) [I. Kononenko, Biological Cybernetics61(5) (1989), 361–370] with two input streams is proposed, with a focus on local inter-layer delaminations identification and characterization.

The present work concerns the study of the effect of variation of the thickness of 2D cylindrical test-piece and lift-off (the air gap between the sensor and the test-piece) on the sensor response in case of transient source current. The coupled circuit’s method employed is based on the mutual’s inductances calculation and with the association to Kirchhoff laws it yields to a transient algebraic equations system which is solved at each time step. The method is applied to the study of the pulsed eddy currents in cylindrical and axisymmetric device with electrical conductivity and geometrical dimensions variations.

In this work, a parametric study is carried out on carbon fiber-reinforced plastic (CFRP) materials to investigate how the spatially varying fiber distribution influences the measured signal in an eddy current testing (ECT) configuration. The measurement setup was modeled using finite element method, while the fiber distribution is taken into account by an inhomogeneous anisotropic conductivity tensor. The study revealed a trade-off relation between the size of the ECT coil and the maximal dynamic range of the ECT signal, which contributes to the understanding of the connection between the fiber arrangement and the ECT signal and provides an opportunity for optimal ECT coil design.

Nondestructive testing (NDT) for damage in thermal barrier coatings (TBCs) is significant for the safety guarantee of gas turbine blades. As a new NDT technology, electromagnetic acoustic transducer (EMAT) is widely applied for NDT of conductive structural components due to its advantages of coupling-free and high adaptability. In this paper, numerical simulations are conducted to study the wave propagation and interaction with delamination defects in TBCs inspected with a Rayleigh wave EMAT of the Lorentz force mechanism. Based on the numerical results, the wave structure in TBC, wave conversion at delamination defect, time domain EMAT signals, and its B-scan images are evaluated and the feasibility of Rayleigh wave EMAT to inspect delamination in TBCs was theoretically clarified.

It is necessary to detect subsurface defects for a key metallic structural component especially a multilayer coating to ensure its structural integrity. In this paper, an imaging algorithm using the synthetic aperture focusing technique (SAFT) is developed for processing surface wave signals of array pickup electromagnetic acoustic transducer (EMAT) to improve its signal-to-noise ratio and detectability of subsurface defects. In addition, an array pickup unit of surface wave EMAT with gap configuration is proposed to receive multi-channel surface wave signals and is optimized by adjusting its coil configuration such as number, spacing and detection distance in order to obtain better SAFT imaging result. Both simulation and measured EMAT surface wave signals are used for the defect imaging and all the results verified the validity and the efficiency of the proposed method.

It is important to measure the global and/or local velocity of an in-pipe metallic flow to control its running state in applications such as a Tokamak fusion reactor. The magnetic field outside the pipe wall will be perturbed by the motion induced eddy current when the liquid metal flows across an applied static magnetic field. This phenomenon gives a possibility to evaluate the in-pipe velocity from the measured magnetic field perturbation signals. In this paper, a non-intrusive velocity evaluation method is proposed accordingly for measuring the velocity of liquid metal through measurement and inversion of the magnetic field surrounding the pipe. An efficient forward simulation method to calculate the magnetic field near a metallic flow in a static environmental magnetic field is developed at first. An inversion scheme based on the singular value decomposition and the L-curve method is then proposed to reconstruct the velocity distribution at a pipe cross-section with the linear equations correlating the flow velocity and the magnetic field regulated using the Tikhonov method. The reconstruction results of pipe flows of different velocity modes verified the feasibility and efficiency of the proposed velocity measurement method for in-pipe metallic flows.

In this work, we present a new non-iterative imaging method for Electrical Resistance Tomography (ERT). The problem in ERT is retrieving the spatial behaviour of the electrical conductivity by means of boundary measurements in steady-state conditions. Specifically, the interest is focused on the inverse obstacle problem, that consists in reconstructing the shape, position and dimension of one or more anomalies embedded in a known background.

The proposed method, called Kernel Method, is based on the idea that if there exists a current density J _n that applied at the boundary ∂𝛺 of the domain under investigation 𝛺 produces the same scalar potential (on ∂𝛺), with and without anomalies, then the power density corresponding to J _n , evaluated on a configuration without anomalies, is vanishing in the region occupied by the latter.

The proposed method has a very low computational cost. Indeed, the evaluation of the desired current density J _n on ∂𝛺 requires a negligible computational effort, and the reconstructions require only one forward problem.

Infrared thermography is an imaging technique that can be used to inspect materials for flaws and various degradations in a non destructive way. In this work, we focused on the use of fast models to recover information about the material properties from experimental measurements recorded over time. Two different modelling approaches are compared to each other and to experimental data acquired on a composite plate. Then, the model based inverse problem consisting in estimating the plate properties is discussed.