Adam D. Fletcher

This work explores the application of Magnetic Induction Spectroscopy (MIS) as a tool for phenotyping cellular structural degradation in biological tissues, specifically plant parenchyma. The principal advantage of MIS is its non-contact operation, whereby induced eddy currents probe bulk electrical properties without physical or electrical contact, avoiding electrode-interface artefacts and sample perturbation. A previously developed differential-coil MIS sensor, with sensitivity optimised via a tuned passive network, was used to measure conductivity spectra from 70 kHz to 9 MHz. Avocado fruit, potato, and apples were subjected to three freeze-thaw cycles as a controlled model of cellular structural degradation. Across the three tissue types, freeze-thaw treatment progressively attenuated the conductivity dispersion, consistent with weakened Maxwell-Wagner interfacial polarisation and loss of cellular compartmentalisation. Correspondingly, dispersion indices, including the model-fitted conductivity dispersion magnitude, Δσ, and the ratio between the low- and high-frequency conductivity, Py, decreased, with Py falling by 40–95% from the fresh state to the third cycle. Bright-field microscopy independently confirmed progressive disruption of cellular boundaries and tissue organisation. These results support that MIS-derived dispersion metrics can provide a quantitative, label-free, non-contact, and volumetric indicator of cellular degradation in bulk biological tissues, with potential applications in biological and agri-food quality assessment.

A positioning system employing visual fiducial markers (AprilTags) has been implemented for use with handheld mine detection equipment. To be suitable for a battery-powered real-time application, the system has been designed to operate at low power (<100 mW) and frame rates between 30 and 50 fps. The system has been integrated into an experimental dual-mode detector system. Position-indexed metal detector and ground-penetrating radar data from laboratory and field trials are presented. The accuracy and precision of the vision-based system are found to be 1.2 cm and 0.5 cm, respectively.

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.

Differential coils are frequently implemented in inductive sensing systems. They can be considered as a single coil that is made up of two or more subcoils, wound in series opposition. They can be used on the transmit or receive side of measurement systems, and, if designed correctly, ensure no coupling between coils under background conditions. By cancelling background coupling, the receive electronics only needs to be able to measure the change in coupling produced by a target. This allows for a more efficient use of the dynamic range, and for larger receive-side amplifier gain, thereby improving SNR. When subcoils are not electrically similar, it can be hard to engineer the coil to be perfectly balanced across a wide bandwidth. This paper presents an analytical model of a resonant differential coil pair that is tested and applied on a planar metal detector for the detection of buried objects. The model demonstrates the capability to balance an arbitrary differential coil pair, which has a broad applicability across a range of inductive sensor applications such as metal detection and non-destructive testing. The method is applied to the practical system. The results show that the correction resulting from this method ensures a stable balance across a significantly enhanced bandwidth. In the case studied here, the bandwidth of the experimental system is increased from 20 kHz to 90 kHz.

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%.

We report on the use of a novel constrained optimisation algorithm for calibrating the finite element model in an eddy current inspection application. An accurate finite element forward model is often important in such eddy current applications for training neural networks or as part of an iterative solver. However, the subject of calibration of the model has not received much attention in the literature to date. We consider a multi-frequency, eddy current depth profiling application, which is important for non-destructive testing in the nuclear industry. In the optimisation algorithm, we use a Levenberg-Marquardt algorithm and a bisection search to ensure constraints are satisfied, coupled with a truncated Gradient and Hessian method. We calibrate two types of finite element models, one using a filament representation for the coils and the other using a full 3D approach. The results show the feasibility of using the constrained non-linear optimisation algorithm for tuning the finite element model parameters. The mean signal-noise ratio after tuning on a truncated spectrum was 29.47 dB for the 3D model and 28.96 dB for the filament; in contrast the mean SNR using the measured coil parameters was 1.48 dB and 4.98 dB for the two uncalibrated models, respectively. The results show that a filament model is competitive with a 3D model of the coils (within the nuclear graphite application); therefore, a computationally faster filament model can be used with minimal effect on accuracy.

This paper discusses an artificial neural inversion approach for non-destructive testing of the graphite moderator bricks that make up the cores of Advanced Gas-Cooled Reactors (AGR). The study employed fully connected feedforward neural networks consisting of four hidden layers and trained with backpropagation approach using Levenberg-Marquardt optimisation algorithm. The approach is based on multi-frequency (MF) eddy current (EC) measurements, and combinations of simulated and measured datasets from a laboratory sample and one of the operating reactor core, along with different regularisation parameters were used to train and test the networks. Various types of artificially generated errors were added to the data during training procedures, which in turn allowed for error tolerance and improved the generalisation of the ANNs to unseen test datasets. First, the ANN was tested using unseen simulated data, followed by the measurements collected from laboratory sample and one of the operating reactor core. The first test from the unseen simulated data showed mean profile error ranging between 1.30% and 8.20%, whereas the profiles estimated from reactor core measurements showed mean profile error ranging between 1.84% and 17.80% when compared with the resistivity measurements from trepanned graphite sample taken out of the reactor core. Further comparison of the network outputs against the profiles estimated using traditional iterative inversion algorithm indicates reasonable agreement between the two approaches with the exception of one case, but the solution time for the ANN was found to be over three orders of magnitude faster than the iterative inversion algorithm.