Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or involving post-processing stages degrading its real-time capability. Additionally, when quantifying the void fraction or porosity, the image may not be required. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained to provide 95% accuracy on this dataset. On the other hand, CNN classification using multi-dimensional data produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

The work presents a perspective in evaluating electromagnetic tomography reconstruction through a spectral eddy current imaging arrangement. Embarking from an established analytical basis, the spectroscopic relation of a metallic conductive body to its physical properties is revealed via multi-frequency mutual impedance measurement. Characteristics are evident, from either modelling or experiment, on certain frequency ranges that discriminate the object’s circumstances. Both the amplitude ratio and phase-contrast image spectrum show information on the conductivity and structure of a target considered pivotal for industrial applications. Two test cases are reported: liquid metal structure determination, and contactless temperature evaluation of a remote/hidden medium/object. Using eddy current based spectroscopic imaging data and appropriate calibration, this work for a first time demonstrates a novel thermal mapping system. This is a wireless and inductive based temperature mapping device that can have great potential applications where none of the existing thermal measuring devices could work noninvasively.

Magnetic induction tomography (MIT) is a powerful imaging system for monitoring the state of metallic materials. Tomographic methods enable automatic inspection of metallic samples making use of multi-sensor measurements and data processing of eddy current-based sensing from mutual inductances. This paper investigates a multi-frequency MIT using both amplitude and phase data. The image reconstruction algorithm is based on a novel spectrally-correlative total variation method allowing an efficient and all-in-one spectral reconstruction. Additionally, the paper shows the rate of change in spectral images with respect to the excitation frequencies. Using both spectral maps and their spectral derivative maps, one can derive key structural and functional information regarding the material under test. This includes their type, size, number, existence of voids and cracks. Spectral maps can also give functional information, such as mechanical strains and their thermal conditions and composition.

This paper presents method to implement Linear Back Projection (LBP) algorithm in Field Programmable Gate Arrays (FPGA). Top-down approach has been adopted for the design of the hardware of LBP algorithm. The FPGA used is Xilinx Spartan 3A and the language used to design the hardware is VHSIC Hardware Description Language (VHDL). The final design is able to reconstruct a 32×32 pixel image from 8-electrode Electrical Capacitance Tomography (ECT) with speed of 23809 slice images per second and the image is shown on LCD. It could be further extended to form quasi 3D image with 32 slices at rate 744 frame-per-second.

Data acquisition system with single signal conditioning for multi-electrode on electrical capacitance volume tomography (ECVT) was described. ECVT is a method in 3D process imaging based on electrical capacitance. The ECVT hardware is employed to measure capacitance signal from 32 electrodes as sensory module. In this investigation, one signal conditioning was used for all electrodes hence hardware construction is simplified and power consumption is relatively low. In order to achieve that, switch configuration which could form analog multiplexer with small resistance and capacitance is required. Each electrode needs 4 analog switches, so that for 32 electrodes 128 analog switches are needed. The electrode is selectable according to desired function as excitation or detection. Experiment result has shown that this ECVT system is able to accomplish capacitance measurement with resolution 0.42 fF.

This letter proposes magnetic induction tomography (MIT) sensors for liquid metal visualization. Design aspect and performance evaluation, which by far were overlooked, are presented to quantify the shape of conductive substance against a nonconductive region. Portable MIT sensors have been constructed and tested on GaInSn in the submerged entry nozzle model. Reconstruction result shows applicability for liquid metal imaging. This feature meets the need to observe flow regime and phase distribution of steel in continuous casting. There are challenging flow regimes that cannot yet fully be recovered, which are also investigated here.