This paper presents the design of a highly-integrated battery-powered portable instrument system for measuring magnetic Barkhausen noise (MBN) across a wide frequency range (1 – 100 Hz). The hardware and software architecture of the system is described in detail. Two miniaturized sensors are designed for taking measurements on flat surfaces and gear tooth slots respectively. Furthermore, a revised skew Gaussian distribution for curve-fitting the MBN signal envelope is proposed. Additionally, the measurement results are analysed using standard error analysis techniques, including standard deviation, relative standard deviation, and box plots, to evaluate the repeatability of the measurements. The accuracy and repeatability of the system under varying excitation frequencies are validated by comparing with a commercial product. Finally, representative MBN signal measurements are presented to demonstrate the system's capability in distinguishing materials with varying hardness levels, as well as in scanning weld cross-section for hardness mappings and residual stress evaluations.

Radar polarimetric imaging for non-destructive testing is a powerful and flexible tool that can be used to enhance the detection of internal structures. In this study, reinforced autoclaved aerated concrete (RAAC) is measured using a polarimetric system in three different acquisition modes—two downward-looking and one sideways-looking configurations, each at a different height. Each acquisition mode is compared and new polarisation states are created using the principle of polarisation synthesis. Images of the internal structures are created using a 3D imaging algorithm, which are used for the analysis. The comparison between acquisition modes demonstrates that using a higher lift-off and polarisation synthesis could offer more flexible operation in the field, allowing the use of handheld detectors and drone-based systems for inaccessible areas. Additionally, the sideways-looking data captured both horizontal and vertical reinforcement and were detected within a single polarisation channel; this configuration also has reduced clutter from the air–concrete boundary, providing a viable option for single polarisation systems.

The Jiles–Atherton (J–A) model has seen extensive use for modelling the hysteresis behaviour of ferromagnetic materials due to its computational efficiency, simplicity of use, and small number of physically related parameters. However, in this work, the application of the J–A model to hysteresis curves obtained from experimental measurements for as-quenched and quenched-and-tempered engineering steels is considered. It has been demonstrated that the current form of the J–A model is not capable of representing certain observed features in the obtained hysteresis curves of these steels, in particular, the rapid narrowing of the loops seen for as-quenched steels and the sharp corners seen for quenched-and-tempered steels. This work has shown that a superior fit to the major loops for such steels can be obtained by applying Gaussian variations with respect to the applied magnetic field to the model parameters. The findings are supported by experimental results from engineering steels used in the oil and gas industry.

Extended reality, encompassing virtual, augmented, and mixed reality, is rapidly transforming industrial workflows in line with the principles of Industry 4.0. This paper presents a focused review of extended reality technologies in the context of manual nondestructive evaluation inspections, highlighting key areas of research and industrial deployments. It surveys the current extended reality hardware landscape, emphasising the sector's drive towards more compact and lightweight form factors. Traditional inspection procedures, such as ultrasonic and eddy current testing, can be augmented with real-time data visualisation and optical tracking methods to digitise the workflow automatically. Applications including 'X-ray vision' for structural maintenance, contextual information overlays, and remote collaboration tools are examined, illustrating how extended reality can reduce operator workload and enhance inspection accuracy. Challenges related to precision tracking, ergonomics, visual clarity, and data privacy are also discussed. Despite hardware and adoption barriers, extended reality offers significant potential to modernise manual testing workflows by integrating digital twin concepts, improving human–machine interaction, and supporting traceable, standardised reporting. This review concludes that, with continued technological and regulatory development, extended reality is poised to become a key enabler of operator-centred inspection systems.

Metallic cylinders are extensively used across a range of industries. The inspection of their properties through eddy current testing (ECT) is crucial to ensure the desired performance of the piece in practical applications. This article proposes for the first time an analytical model for the mutual inductance variation of a coil pair encircling an eccentric metallic cylinder, applicable to 3-D asymmetric cases where vibration and wobble exist. The analytical solution is further simplified for faster calculation while maintaining high consistency with the complete model. Moreover, an inverse approach is proposed to simultaneously measure rod conductivity and its eccentricity from the center based on the simplified analytical model, exploiting the crossing frequency between the real and imaginary parts of the inductance spectra. A modified Newton-Raphson method is employed to reduce the estimation error further. Experiments are carried out using a multifrequency eddy current sensor to test different metallic specimens, the results of which validated the effectiveness of the analytical solution. Finally, the proposed inverse approach achieves high-accuracy estimations for both conductivity and eccentricity.

In this study, we propose ECTAR, an innovative system integrating augmented reality (AR) and eddy current testing (ECT) techniques to enhance inspection efficiency. Traditional ECT methods often encounter limitations, including inefficiencies in providing intuitive real-time visualization. The proposed AR+ECT system addresses this challenge by overlaying defect maps onto the actual object surface under inspection as the scan is carried out, allowing inspectors to visualize defects instantly on the physical surface via the AR display device. The system utilizes AprilTag markers for precise pose estimation. The effect of the number of markers and the distance between the AR display device and the markers are evaluated. Experimental results demonstrate the system’s ability to operate at 30 frames per second with high image quality, achieving a rotational error of just 1.17 degrees and a translational error of 1.42 mm.

In non-destructive testing (NDT), inspection of complex metallic parts is a challenge. At the moment, the object ' s surface profile is typically obtained from design documents or through separate dedicated measurements such as optical methods. Previously we have used contactless inductive sensing and real time feedback to control a robot to follow unseen complex smooth surfaces in a non-contact autonomous manner. In this work, further progress is made to increase the capacity of the system so that it can transverse a sharp edge. A hemi-spherically-arranged multidirectional eddy current array sensor is devised for this purpose. The array sensor is fixed to the end-effector of a UR5 robotic arm, and its signals are used to control the pose of the robotic arm, keeping its axis perpendicular to the local surface and maintaining a constant lift-off distance when traversing a sharp edge, both for convex and concave ones. The metallic surface profile can then be reconstructed from the scanning trajectory of the robotic arm. The resulting sensor pose is also used to estimate the edge direction, enabling integrated reconstruction of both geometry and orientation. Experiments on copper foil samples with both convex and concave edges at 120°, 90°, and 60° demonstrate accurate reconstruction results. The acquired scanning trajectories closely follow the true surface profiles and exhibit symmetric arc transitions at edge regions. For a 90° edge, the maximum profile measurement error is less than 3 mm, and the estimated edge orientation deviates less than 1°.

The continuous annealing process is widely used in the production of advanced high-strength steels. However, to tightly regulate the mechanical properties of the steel, precise control of processing parameters is needed. Although some techniques are available to monitor the mechanical properties of the steel on entry and exit to the furnace, monitoring the evolving microstructure of the steel through installation of sensors in the annealing line is extremely challenging due to the high temperature, high speed of the steel strip and limited space in the furnace. This study presents the development and validation of a multifrequency electromagnetic sensor system for real-time monitoring of microstructural transformations in steel during thermal cycling, intended for deployment in a continuous annealing line. Experiments were conducted on austenitic stainless steel to study the signal response to an increase in resistivity without a change in magnetic permeability. Pure nickel was tested to investigate the response to a change in magnetic permeability and the ferromagnetic-to-paramagnetic transition at its Curie temperature. A ferritic stainless steel was also tested to assess the performance of the system for high-temperature ferromagnetic materials and a higher-temperature ferromagnetic-to-paramagnetic transition. The tests indicate a strong response to material resistivity and permeability changes, with complementary information from different frequencies. Test results are supplemented by a finite element modelling study into the effect of a change in frequency and permeability on sensor response, with a discussion on the implications of experimental and modelling results for future applications. The results show that the developed system has the potential to characterise thermally induced changes in steels, establishing proof of concept for non-destructive, high-temperature electromagnetic sensing in steel processing and setting the foundation for further industrial deployment in phase and recrystallisation monitoring.

This paper reports a study exploring microwave Inverse Synthetic Aperture Radar (ISAR) imaging of biological specimens, with the longer-term goal of assessing its applicability for non-invasive and non-destructive imaging of the human brain in the context of stroke detection and monitoring. The paper describes the design and fabrication of a laboratory testbed developed to examine the feasibility of the ISAR approach. The system includes a custom antenna designed to reduce self-generated clutter and support the imaging process. Water was used as a matching medium due to its specific permittivity-frequency relationship, providing controlled conditions for experimental evaluation. The forward and inverse models were initially tested in simulated environments, and subsequently evaluated using physical measurements on real biological specimens in a bistatic radar configuration, to assess their ability to localize internal anomalies with sub-centimetre resolution across a 26 cm circular imaging area. The reconstructed images from vegetable phantoms such as potatoes and turnips suggest the technique may be capable of detecting internal structural variations. These preliminary findings serve as a foundation for future investigations into human brain imaging applications.

Pulsed eddy current has been extensively utilized in metal industry for non-destructive testing due to its distinctive benefits, including fast response, good penetration ability and lift-off tolerance. Eddy current diffuses in a complicated manner, with the diffusion velocity fluctuating over spatial and temporal dimensions. Gaining a comprehensive view of the diffusion velocity field enhances physical insights into pulsed eddy current detection. An explicit analytical equation for calculating the time domain magnetic vector potential in metal plates through inverse Laplace transform is derived, which employs an innovative and more efficient method to calculate the root of the phase term. It shows advantages in terms of speed compared with the traditional inverse Fourier transform. A novel whole-field gradient method is proposed for diffusion velocity field calculation, which divides the whole diffusion process into three stages. This method greatly improves computational efficiency over the previous point-tracing method by calculating the gradient of eddy current contours to obtain the overall velocity field in a single step, rather than tracking individual points. The calculated results align with the phenomena observed in numerical simulations. Additionally, the proposed method has been used to analyze the relationship between the properties of the planar conductor, probe lift-off, and the diffusion velocity field.

Disposable vapes pose an environmental and fire hazard to waste streams when disposed of incorrectly. The lithium battery inside disposable vapes can produce an exothermic reaction when the lithium inside the battery is inadvertently exposed to air and moisture. New sensing technologies may be needed to screen waste streams for these vape hazards and this paper considers the potential of inductive techniques based on the magnetic polarisability tensor (MPT) representation. The MPT can be described by three complex components based on a target regardless of orientation. In this paper, the rank 2 MPT is measured and calculated for 10 vapes and 37 batteries for 28 logarithmically spaced frequencies from 119 Hz to 95.4 KHz. The 168 features of each object are reduced down to 2 features using principal component analysis (PCA) and linear discriminant analysis. The reduction of the features allows for the visualisation and grouping of the objects. Three clear groups of objects can be seen when the maximum feature scales the measurement and a two-component PCA transform is applied. The first group is the vapes, which are grouped away from the other batteries. The second is the batteries, which are grouped by size. Finally, zinc batteries are grouped away from the rest due to their case material.

Eddy-current testing technique has been extensively explored for estimating the electromagnetic property of steel plates in various industrial applications. In this article, a physics-guided deep learning (DL) method is proposed to estimate the permeability of plate in high thickness with probe liftoff. A simplified analytical model is derived, which realises the single to multiple frequency inductance transformation and calculates the related physical properties of the measurement configuration. A constant is found, which is a fundamental coefficient describing the first-order nature of the sensor response to a plate and it is insensitive to plate properties and probe dimensions. The nonlinear mapping from physical information, derived from the simplified analytical model, to plate permeability is constructed by the DL model based on the modified ResNet18-1D. Numerical simulations and experiments have been performed to evaluate the proposed method for permeability estimation with various plate materials and probe liftoff. The method achieves real-time accurate estimation of plate permeability with a relative error lower than 3%.

The frequency signature of the scattered fields from an object remains a crucial element for the proper identification and classification of the same. As a solution to the identified issues with commercial software packages for solving the scattered fields from metallic scatterers for multiple incident angles at once, this paper proposes an efficient numerical model to solve for the same in an Ultra-Wideband (UWB) regime. The paper begins with the theoretical formulation of the integral equations governing the electromagnetic scattering phenomenon. We then discretize and solve for the surface current densities. Finally, the results of the numerical model are compared with a commercial package, showing a strong statistical agreement characterized by a relatively low mean and low standard deviation for the modeling error. A similar conclusion can be drawn from the comparison between the numerical model and experimental results, further validating the accuracy and reliability of the proposed model.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

The introduction of polarimetric analysis to Ultra- Wideband (UWB) have attracted much research interest due to its ability to exploit resonant responses and enhance detection capabilities in Concealed Object Detection (COD) and other radar applications. Since the correct characterization of the unique features of objects is a crucial element in the proper identification and classification of the same, this paper proposes the use of the Jones matrix, which fully describes the polarimetric interaction of the scattered electromagnetic field and objects in a monostatic configuration, for the extraction of their frequency signatures. The paper begins with the formulation of an algorithm to obtain the basis independent Jones matrix in terms of a transformation by a rotational matrix. Principal Component Analysis (PCA) is then performed on the results to extract the principal values which constitutes the frequency signature of objects. A comparison of UWB frequency signatures of different objects with different geometries as well as the same obj ects under different orientations have been presented.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

Although much information can be gained about thermally induced microstructural changes in metals through the measurement of their thermophysical properties using a differential scanning calorimeter (DSC), due to competing influences on the signal, not all microstructural changes can be fully characterised this way. For example, accurate characterisation of recrystallisation, tempering, and changes in retained delta ferrite in alloyed steels becomes complex due to additional signal changes due to the Curie point, oxidation, and the rate (and therefore the magnitude) of transformation. However, these types of microstructural changes have been shown to invoke strong magnetic and electromagnetic (EM) responses; therefore, simultaneous EM measurements can provide additional complementary data which can help to emphasise or deconvolute these complex signals and develop a more complete understanding of certain metallurgical phenomena. This paper discusses how a DSC machine has been modified to incorporate an EM sensor consisting of two copper coils printed onto either side of a ceramic substrate, with one coil acting as a transmitter and the other as a receiver. The coil is interfaced with a custom-built data acquisition system, which provides current to the transmit coil, records signals from the receive coil, and is controlled by a graphical user interface which allows the user to select multiple excitation frequencies. The equipment has a useable frequency range of approximately 1–100 kHz and outputs phase and magnitude readings at a rate of approximately 50 samples per second. Simultaneous DSC-EM measurements were performed on a nickel sample up to a temperature of 600 °C, with the reversable ferromagnetic to paramagnetic transition in the nickel sample invoking a clear EM response. The results show that the combined DSC-EM apparatus has the potential to provide a powerful tool for the analysis of thermally induced microstructural changes in metals, feeding into research on steel production, development of magnetic and conductive materials, and many more areas.

The detection of buried objects with GPR poses a significant challenge in many sectors, including utilities, non-destructive testing, archaeology, military operations and humanitarian efforts. It is a difficult task partly due to the presence of clutter and the strong signal attenuation presented by many soil types. This paper seeks to improve the detection of buried objects using the combination of Synthetic Aperture Radar (SAR) and Polarimetry (PolSAR). In this study a Stepped Frequency Continuous Wave (SFCW) air-coupled radar is used to acquire polarimetric measurements of buried metallic and dielectric objects between the frequency range of 1 - 6.5 GHz. A 3D Synthetic Aperture Radar (SAR) algorithm is developed and following a polarimetric calibration procedure the SAR algorithm is used to create sub-surface images of each polarization channel. Using polarimetric decompositions, the dominant scattering mechanisms are identified and are used to synthesize polarization signatures of the buried objects. Analysis is conducted to determine the optimal polarization state for sub-surface detection, enhancing target identification and discrimination capabilities.

Optimization of the yield of crops is essential for the security of the food supply and the efficiency of farming. This paper examines some of the issues and challenges involved with the measurement of the potato tubers within the soil using ground penetrating radar (GPR) in the U.K. An order of magnitude assessment of the received signal levels from single or multiple groups of potatoes is provided. The antenna configurations are based on loaded dipole antennas near the potato ridge surface. Measurements of potato tubers at two test sites in the U.K. are described, as well as an approach to signal processing to optimize detectability. The article provides a systematic study of GPR techniques for the monitoring of tuber growth.

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target’s polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems.