Eddy Current Imaging Research Articles

Theory and mitigation of motional eddy current in high-field eddy current shielding.

Eddy current shielding by a Faraday cage is an effective way to shield alternating-current magnetic fields in scientific instrumentation. In a strong static magnetic field, however, the eddy current in the conductive shield is subject to the Lorentz force, which causes the shield to vibrate. In addition to mechanical issues (e.g., acoustic noise), such vibration induces motional eddy current in the shield that can dominate the original, electromagnetic eddy current to undermine the conductor's shielding capability. In this work, we investigate a method to control motional eddy current by making cut-out patterns in the conductor that follow the electromagnetic eddy current image. This effectively limits the surface current of the plate to a single mode and prevents the proliferation of uncontrolled motion-induced surface currents that disrupts eddy current shielding. After developing a comprehensive theory of magneto-mechanical interaction in a conductive plate, the proposed method was tested on a flat-geometry testbed experiment inside a 3 T magnetic resonance imaging (MRI) magnet. It was found that the magnetic field generated by the motional eddy current was much more localized in space and frequency for a patterned-copper shield compared to a solid copper. The magnetic field of the patterned shield could be accurately predicted from the impedance measurement in the magnet. Implications of our results for improved shielding of gradient fields in high-field MRI are discussed.

Static eddy current imaging for nondestructive testing of aeronautical structures

Static eddy current imaging for nondestructive testing of aeronautical structures

Image analysis and CNN-based crack depth estimation using eddy current data

This study presents a comprehensive approach for crack depth estimation utilizing advanced image analysis techniques and a Convolutional Neural Network (CNN) model. The aim is to enhance accuracy and reliability in predicting crack depths, particularly for sub-millimeter cracks. The research addresses challenges arising from noise in images by employing a pre-processing technique and augmentation methods. The proposed method's effectiveness is showcased through its application to experimental crack data from diverse specimens. The outcomes exhibit a Mean Relative Error (MRE) of around 6%, indicating a high level of precision. These results affirm the potential of the methodology for real-world industrial applications. Additionally, the study explores the integration of eddy current image processing with CNN for Non-Destructive Evaluation (NDE) problems, offering a new approach for tiny surface-crack detection and characterization.

PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging.

PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for non-commercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

Reconstruction of Metal Defect Images Based on the Sensitivity Matrix of High Conductivity Initial Estimate for Eddy Current Tomography

Reconstruction of Metal Defect Images Based on the Sensitivity Matrix of High Conductivity Initial Estimate for Eddy Current Tomography

Monotonicity of the Transfer Function for Eddy Current Tomography

Monotonicity of the Transfer Function for Eddy Current Tomography

Cross-Correlation Inspired Residual Network for Pulsed Eddy Current Imaging and Detecting of Subsurface Defects

The decay of pulsed eddy currents (PEC) with depth in diffusion decreases resultant signal changes, thus bringing a challenge for reliable and accurate evaluation of deep subsurface defects. In this work, a novel cross-correlation inspired residual network, termed CCResNet, is proposed to improve the capability for smart evaluation of subsurface defects. It consists of a cross-correlation layer, a residual network, and a novel loss function, namely, focal-probability of detection (Focal-POD) loss. The customized Gaussian wavelet basis enables us to derive weak features from heavily noised PEC signals due to the similarity by cross-correlation operation, which is the origin of the constructed cross-correlation layer. Then, a Focal-POD loss is proposed to address class imbalance and endow CCResNet with powerful capability for detection of deep subsurface defects by increasing their loss values. Finally, a semi-supervised framework is built to re-train CCResNet using pseudo and labelled dataset to obtain classified results as imaging features. The experimental results show that the developed CCResNet is featured as better imaging resolution, more accurate evaluation, and intelligence in detection of deeper subsurface defects.

Surface Profile Reconstruction of Complex Cracks Using the Signals of Rotating Eddy Current Testing Through the Eddy Current Imaging Method

In industry, natural cracks usually exist in complex forms such as adjacent, colony, irregular and crossing. Mere magnetic field is inadequate to accurately evaluate the complex cracks using eddy current testing. In this study, an improved eddy current imaging method using the signal of rotating eddy current testing was proposed to reconstruct the surface profile of complex cracks. A transformation method was adopted to transform the signals of rotating eddy current testing into those under uniform eddy current testing at the desired orientation. The deconvolution of the transformed signals and square current dipole was carried out to obtain eddy current images at eight orientations around complex cracks. By accumulating the eddy current images, the surface profile of the complex cracks was reconstructed. Simulations and experiments were carried out to illustrate, validate, and test the proposed method. The results indicated that the obtained surface profiles were clearer than those obtained by previous method using uniform eddy current testing. The improved method was effective for artificial complex slits and irregular stress corrosion crack colonies. Moreover, its performance was stable for the cracks with different angles and depths.

Eddy Current Methodology for Nondestructive Assessment of Thickness of Silicon Carbide Coating on Carbon-Carbon Composites

Eddy current (EC) methodology has been developed for non-destructive assessment of thickness of Silicon carbide (SiC) coating on carbon-carbon (C/C) composite specimens. For coating, polymer precursor route has been employed and approximate coating thickness has been determined by weight gain method. EC probe signal amplitude has been measured before and after the coating. Despite scatter in the baseline data due to surface roughness on C/C specimens, EC methodology could identify undercoated (thickness <20 µm) specimens. A calibration graph has been established between EC signal amplitude and coating thickness (weight gain) to evaluate the performance of the EC methodology. The methodology has evaluated the coating thickness with error less than ±5 µm. Studies confirm that it is possible to assess the efficacy of coating process and to readily identify thinly coated regions using eddy current imaging.

Theoretical and Numerical Study of Eddy Current Pulsed Thermography to Detect Damage of Deep-Sea Manned Pressure Hull

At present, research on pressure hull safety is mainly focused on the constitutive model of material properties and the evaluation model of structural parameters aiming at fatigue life prediction. The damage identification and quantitative evaluation methods of pressure hulls have not been studied. In this study, an eddy current thermal imaging method is introduced to detect micro-cracks in a deep-sea spherical pressure hull. In the detection method, temperature is used as a parameter to identify and quantify cracks. The temperature distribution around the cracks is studied using theoretical analysis and finite element simulation. A theoretical model is established using electromagnetic theory and heat transfer theory. Moreover, the temperature difference between the cracked area and the non-cracked area can be obtained by solving the heat conduction equation. A pulsed eddy current thermal imaging testing system is established, and a defective titanium alloy specimen is tested. At the same time, the temperature around the cracks in the specimens is simulated. The specimens have the same material and welding as a deep-sea spherical pressure hull. This paper discusses the possibility of its use in a pressure hull, which will provide a reference for micro-crack damage identification and quantitative evaluation of a deep-sea spherical pressure hull.

Feature fusion for pulse-modulation eddy current imaging and evaluation of defects in layered conductors

In this paper, the imaging and evaluation of defects hidden within the stratified conductor via pulse-modulation eddy current testing (PMEC) is intensively scrutinised through theoretical and experimental investigation. Following the proposition regarding the optimal excitation signal of PMEC, a fused signal feature integrating the harmonic and pulsed characteristics of PMEC signals is proposed in an effort to further enhance the testing sensitivity and signal-to-noise ratio (SNR). By comparing with the features of conventional PMEC and pulsed eddy current testing (PEC), the superiority of the fused feature is identified via theoretical simulations. This is subsequently supported by the corrosion imaging results obtained from the experiments, further indicating the advantage of the fused feature over traditional features in terms of testing sensitivity and SNR.

Improving Magnetic Field Response of Eddy Current Magneto-Optical Imaging for Defect Detection in Carbon Fiber Reinforced Polymers

A large number of carbon fiber reinforced polymers have been applied to aircraft and automobiles, and many nondestructive testing methods have been studied to detect their defects. Eddy current magneto-optical imaging nondestructive testing technology has been widely used in the detection of metal materials such as aircraft skin, but it usually requires a large excitation current and, at present, can only detect metal materials with high conductivity. In order to take full advantage of the innate benefits and efficiency of eddy current magneto-optic imaging and enable it to detect defects in carbon fiber reinforced polymers with weak conductivity, it is necessary to improve the magnetic field response of the eddy current magneto-optic imaging system and explore suitable excitation and detection methods. The scanning eddy current magneto-optical imaging nondestructive testing device built in this study has improved the magnetic field response of the system, and the eddy current magneto-optical phase imaging testing method has been proposed to detect the crack defects of carbon fiber reinforced polymers. The effectiveness of the method has been verified by simulation and experiment.

3D surface crack characterization by eddy current array image and a fast algorithm search

Abstract Nowadays, 3D eddy current nondestructive characterization of crack and corrosion defects while using ECA remains an industrial challenge because the obtained image permits to determine only the 2D defect shape. Consequently, this article is devoted to determine directly the crack length and width by eddy current images through sensor array. Afterwards, we extract the maximal impedance amplitude to estimate the crack depth while using the deterministic algorithm that we have recently developed. In fact, the obtained results have demonstrated the effectiveness and the reliability of the proposed method.

Automatic Detection and Imaging of Rivet Hole Defects for Aircraft Structures With Optimized Sensor Array Using Eddy Current Method and Image Analysis

Fatigue cracking in riveted lap joints on aircraft could cause severe safety consequences in flight. The prior inspection for the integrity of the second layer of aircraft structure with rivet removed is necessary due to the priority of safety. Methods such as radiographic and ultrasonic testing are widely applied but require relatively expensive equipment, mechanical scanning or professional operators. Eddy current method can be adopted for non-destructive testing (NDT) inspection, which is safe and low-cost. In this paper, a hand-held eddy current (EC) sensor array was optimised based on sensitivity map analysis. The EC sensor array was used with a multi-channel EM instrument, which was capable of inspecting defects around the fastener locations covered by the head of rivets. A simulated sample of riveted joint with defects was examined with the eddy current sensing system and the collected data was processed by Hough Circle Transformation (HCT) and texture analysis. The analysis results could detect and locate fatigue cracks on the sample. The presented eddy current sensing system in this paper achieved an automatic, real-time, portable, convenient and accurate inspection method for defects in riveted joints.

Diagnosis of fatigue cracks in structural elements manufactured by additive manufacturing

Recently, the production of metal structural elements using additive processes is gaining popularity. They allow the production of an element of any geometry and structure, while reducing the mass of the produced part. Unfortunately, the AM technology causes structural elements to be burdened with numerous defects in the form of porosity, unmelted powder, weak interlayer bonds or residual stresses, etc. These defects translate into the possibility of an early, uncontrolled fatigue crack. For this reason, it is important to find ways to quickly detect damage caused by random variable loads. The work presents methods of diagnosing cracks and fatigue damage in structural elements produced using 3D printing. The division of research methods allowing for the detection of defects in structural elements and the characteristics of the most popular methods, taking into account their advantages and limitations, were presented. Methods appropriate for a given type of material, test conditions and damages are indicated. The most important methods of diagnosing defects in printed elements include: penetrating, ultrasonic, radiographic, eddy current and thermal imaging methods.

Modelling and evaluation of carbon fibre composite structures using high-frequency eddy current imaging

The mechanical performance of carbon fibre reinforced polymer (CFRP) composites is determined by the stacking sequence of specifically orientated ply lamina layers. In these multi-layer structures, global ply misalignment and local fibre waviness can occur during the manufacturing process, leading to reduced performance and structural failure during operation. High-frequency eddy-current testing (ECT) has previously demonstrated its capability for detecting orientation-related features, including fibre orientation and waviness. However, accurate sensor optimisation and inversion of orientation & material structure cannot be achieved without proper, validated modelling techniques to simulate CFRP features. The lack of suitable models is in part due to CFRP exhibiting highly-complex electromagnetic interactions between fibres, lamina and the ECT sensors, which are challenging to integrate. This work proposes a novel finite element modelling approach to simulate the ECT response to planar multi-layered CFRP components. The fibre tow structure of each unidirectional ply is modelled using orientation dependant 2D conductivity tensor waveforms, and virtual 2D ECT scans are simulated by shifting the waveforms within the model mesh. The results demonstrate that idealised electromagnetic characteristics of the CFRP structure can be successfully modelled compared with experimental data and that 2D ECT data of complex CFRP layers structures can be simulated with improved computational speeds, up to 5x faster compared to standard approaches. Automated data-analysis tools, including Radon transform (RT) and 2D FFT, are employed to validate the simulated 2D scan data through the characterisation of fibre orientations and simulated 2D scans used to evaluate the orientation inversion techniques. The results demonstrate that RT analysis detects fibre orientations with better accuracy, precision and consistency than equivalent 2D FFT analysis techniques. The simulation also demonstrates the reduced resistivity losses compared with isotropic materials caused by the different heterogeneous and multi-layer structures. It predicts high current densities at interfaces of plies with orthogonal orientations, resulting in an effective interface skin-depth.

Gradient-field pulsed eddy current imaging of hidden corrosion in layered conductors via Sparse Bayesian Learning alongside Baseline Estimation and Denoising with Sparsity

Layered structures of nonmagnetic materials such as the aluminium alloy are broadly used in the engineering field such as aerospace, energy, etc. Whereas, the corrosive environment leaves in-service layered structures inevitably prone to the subsurface corrosion which poses a severe threat to the structural integrity. Therefore, it is indispensable to quantitatively evaluate corrosion via non-destructive evaluation techniques. Gradient-field Pulsed Eddy Current technique (GPEC) has been found to be advantageous for high-sensitivity inspection of hidden corrosion. In this paper a Planar Current Sheet (PCS) probe of GPEC is proposed with the optimised trace layout for implementation of high degree of field uniformity. Following the acquisition of the raw corrosion image with the optimised GPEC probe, an image processing algorithm integrating the Sparse Bayesian Learning (SBL) with Baseline Estimation and Denoising with Sparsity (BEADS) has been proposed for reproduction of corrosion images conceiving the information regarding the corrosion-depth profile. The investigation via simulations and experiments reveals that through the 2D scanning of the proposed PCS probe of GPEC, the raw corrosion images and reproduced images derived by using the proposed SBL-BEADS algorithm simultaneously gives the comprehensive evaluation of the subsurface corrosion hidden within the layered structures in terms of the corrosion-opening and corrosion-depth profiles, respectively.

Detection of Surface and Subsurface Flaws with Miniature GMR-Based Gradiometer.

The eddy-current (EC) testing method is frequently utilized in the nondestructive inspection of conductive materials. To detect the minor and complex-shaped defects on the surface and in the underlying layers of a metallic sample, a miniature eddy-current probe with high sensitivity is preferred for enhancing the signal quality and spatial resolution of the obtained eddy-current images. In this work, we propose a novel design of a miniature eddy-current probe using a giant magnetoresistance (GMR) sensor fabricated on a silicon chip. The in-house-made GMR sensor comprises two cascaded spin-valve elements in parallel with an external variable resistor to form a Wheatstone bridge. The two elements on the chip are excited by the alternating magnetic field generated by a tiny coil aligned to the position that balances the background output of the bridge sensor. In this way, the two GMR elements behave effectively as an axial gradiometer with the bottom element sensitive to the surface and near-surface defects on a conductive specimen. The performance of the EC probe is verified by the numerical simulation and the corresponding experiments with machined defects on metallic samples. With this design, the geometric characteristics of the defects are clearly visualized with a spatial resolution of about 1 mm. The results demonstrate the feasibility and superiority of the proposed miniature GMR EC probe for characterizing the small and complex-shaped defects in multilayer conductive samples.

Multilayer perceptron neural network and eddy current technique for estimation of the crack depth on massive metal structures

This paper introduces a method for estimating the maximum depth (sub-millimeter) of minor cracks on the surface of aluminum plates used in the aeronautical industry. A set of C-scan eddy current (EC) images, including real and imaginary parts of the impedance, is analyzed to extract suitable features after reducing noise effects, such as background noises and edge noises. Based on the obtained features, e.g. maximum impedance, the background feature, background noises, type of sensors, a Multilayer Perceptron (MLP) Neural Network is built to estimate the maximum depth of the cracks. The network is optimized based on loss functions, such as mean absolute error and mean squared error. An optimal network structure with five neurons in the first hidden layer and eight neurons in the second hidden layer is chosen. The obtained result indicated that the relative error of estimations is lower than 10% for almost all experimental tested samples.

Eddy Current Imaging of Fiber Textures in CFRP Composite Based on Spatial Frequency Domain De-aliasing Method

Eddy Current Imaging of Fiber Textures in CFRP Composite Based on Spatial Frequency Domain De-aliasing Method