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Tomographic and Multi-Dimensional Sensors

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 7493

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Special Issue Editors

Prof. Dr. Manuch Soleimani

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Guest Editor

Engineering Tomography Laboratory (ETL), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
Interests: electrical and electromagnetic tomography; X-ray CT; ultrasound tomography; multi-modality tomography, inverse problems and machine learning with applications in industrial tomography; robotic touch sensing and medical imaging
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Brian Hoyle

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Guest Editor

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: multi-dimensional and distributed sensing technologies

Special Issue Information

Dear Colleagues,

Sensors typically provide key control information of critical parameters in manufacturing processes to meet environmental goals of maximum energy efficiency and minimised emissions, coupled with commercial goals of product quality and process plant utilisation. Many processes operate on bulk raw materials which are combined, perhaps over several stages, into the required intermediate or final form. To attain these general goals, it is often important to monitor a whole process space. In some processes, a single-point sensor may be located at a location assumed to represent a whole space, but variations in materials and process operations may invalidate this assumption. Current powerful process control systems have the potential to optimise process operations, but only when supplied with the most complete state data. Multi-dimensional sensors offer this major capability. The Special Issue presents papers which progress this key aim in novel proposals for appropriate multidimensional sensing configurations, typically in terms of spatial and material property values. Such sensor configurations will include data representations to suit on-line process control requirements. They may offer novel configurations of individual or hybrid sensing elements. A combination may offer a rolling time series for a specific spatial distribution, or an integrated view of a preferred composite representative parameter. SE papers should focus on novel sensor arrangement and methodology proposals, rather than whole application proposals, although they may include a wide range of specific materials, condition sensing (pressure, temperature, etc.), and states (gas, liquid, and solids).

Prof. Dr. Manuch Soleimani
Prof. Dr. Brian Hoyle
Guest Editors

Manuscript Submission Information

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Keywords

tomographic sensors
multi-dimensional sensing
process-sensing

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Published Papers (6 papers)

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Research

12 pages, 2220 KiB

Open AccessArticle

Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy

by Xiaohu Ma, Alice Fischerauer, Sebastian Haacke and Gerhard Fischerauer

Sensors 2024, 24(24), 8111; https://doi.org/10.3390/s24248111 - 19 Dec 2024

Viewed by 608

Abstract

Molding sand mixtures in the foundry industry are typically composed of fresh and reclaimed sands, water, and additives such as bentonite. Optimizing the control of these mixtures and the recycling of used sand after casting requires an efficient in-line monitoring method, which is currently unavailable. This study explores the potential of an AI-enhanced electrical impedance spectroscopy (EIS) system as a solution. To establish a fundamental dataset, we characterized various sand mixtures containing quartz sand, bentonite, and deionized water using EIS in the frequency range from 20 Hz to 1 MHz under laboratory conditions and also measured the water content and density of samples. Principal component analysis was applied to the EIS data to extract relevant features as input data for machine learning models. These features, combined with water content and density, were used to train regression models based on fully connected neural networks to estimate the bentonite content in the mixtures. This led to a high prediction accuracy (R² = 0.94). These results demonstrate that AI-enhanced EIS has promising potential for the in-line monitoring of bulk material in the foundry industry, paving the way for optimized process control and efficient sand recycling. Full article

(This article belongs to the Special Issue Tomographic and Multi-Dimensional Sensors)

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10 pages, 3727 KiB

Open AccessArticle

Two-Field Excitation for Contactless Inductive Flow Tomography

by Max Sieger, Katharina Gudat, Rahul Mitra, Stefanie Sonntag, Frank Stefani, Sven Eckert and Thomas Wondrak

Sensors 2024, 24(14), 4458; https://doi.org/10.3390/s24144458 - 10 Jul 2024

Cited by 2 | Viewed by 873

Abstract

Contactless inductive flow tomography (CIFT) is a flow measurement technique allowing for visualization of the global flow in electrically conducting fluids. The method is based on the principle of induction by motion: very weak induced magnetic fields arise from the fluid motion under the influence of a primary excitation magnetic field and can be measured precisely outside of the fluid volume. The structure of the causative flow field can be reconstructed from the induced magnetic field values by solving the according linear inverse problem using appropriate regularization methods. The concurrent use of more than one excitation magnetic field is necessary to fully reconstruct three-dimensional liquid metal flows. In our laboratory demonstrator experiment, we impose two excitation magnetic fields perpendicular to each other to a mechanically driven flow of the liquid metal alloy GaInSn. In the first approach, the excitation fields are multiplexed. Here, the temporal resolution of the measurement needs to be kept as high as possible. Consecutive application by multiplexing enables determining the flow structure in the liquid with a temporal resolution down to 3 s with the existing equipment. In another approach, we concurrently apply two sinusoidal excitation fields with different frequencies. The signals are disentangled on the basis of the lock-in principle, enabling a successful reconstruction of the liquid metal flow. Full article

(This article belongs to the Special Issue Tomographic and Multi-Dimensional Sensors)

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20 pages, 3515 KiB

Open AccessArticle

A Coupled Double-Layer Electrical Impedance Tomography-Based Sensing Skin for Pressure and Leak Detection

by Petri Kuusela and Aku Seppänen

Sensors 2024, 24(13), 4134; https://doi.org/10.3390/s24134134 - 26 Jun 2024

Viewed by 1428

Abstract

There is an extensive need for surface sensors for applications such as tactile sensing for robotics, damage and strain detection for structural health monitoring and leak detection for buried structures. One type of surface sensor is electrical impedance tomography (EIT)-based sensing skins, which use electrically conductive coatings applied on the object’s surface to monitor physical or chemical phenomena on the surface. In this article, we propose a sensing skin with two electrically coupled layers separated by an insulator. Based on electrical measurements, the spatial distribution of the electrical coupling between the layers is estimated. This coupling is sensitive to both the pressure distribution on the surface and water entering between the layers through a leak. We present simulations and experimental studies to evaluate the feasibility of the proposed method for pressure sensing and leak detection. The results support the feasibility of the proposed method for both of these applications. Full article

(This article belongs to the Special Issue Tomographic and Multi-Dimensional Sensors)

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12 pages, 5362 KiB

Open AccessArticle

Simplified Beam Hardening Correction for Ultrafast X-ray CT Imaging of Binary Granular Mixtures

by Martina Bieberle, Theodoros Nestor Papapetrou, Gregory Lecrivain, Dominic Windisch, André Bieberle, Michael Wagner and Uwe Hampel

Sensors 2024, 24(10), 2964; https://doi.org/10.3390/s24102964 - 7 May 2024

Cited by 2 | Viewed by 1158

Abstract

Ultrafast X-ray computed tomography is an advanced imaging technique for multiphase flows. It has been used with great success for studying gas–liquid as well as gas–solid flows. Here, we apply this technique to analyze density-driven particle segregation in a rotating drum as an exemplary use case for analyzing industrial particle mixing systems. As glass particles are used as the denser of two granular species to be mixed, beam hardening artefacts occur and hamper the data analysis. In the general case of a distribution of arbitrary materials, the inverse problem of image reconstruction with energy-dependent attenuation is often ill-posed. Consequently, commonly known beam hardening correction algorithms are often quite complex. In our case, however, the number of materials is limited. We therefore propose a correction algorithm simplified by taking advantage of the known material properties, and demonstrate its ability to improve image quality and subsequent analyses significantly. Full article

(This article belongs to the Special Issue Tomographic and Multi-Dimensional Sensors)

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16 pages, 6134 KiB

Open AccessArticle

Modular and Cost-Effective Computed Tomography Design

by André Bieberle, Rainer Hoffmann, Alexander Döß, Eckhard Schleicher and Uwe Hampel

Sensors 2024, 24(8), 2568; https://doi.org/10.3390/s24082568 - 17 Apr 2024

Viewed by 1226

Abstract

We present a modular and cost-effective gamma ray computed tomography system for multiphase flow investigations in industrial apparatuses. It mainly comprises a ¹³⁷Cs isotopic source and an in-house-assembled detector arc, with a total of 16 scintillation detectors, offering a quantum efficiency of approximately 75% and an active area of 10 × 10 mm² each. The detectors are operated in pulse mode to exclude scattered gamma photons from counting by using a dual-energy discrimination stage. Flexible application of the computed tomography system, i.e., for various object sizes and densities, is provided by an elaborated detector arc design, in combination with a scanning procedure that allows for simultaneous parallel beam projection acquisition. This allows the scan time to be scaled down with the number of individual detectors. Eventually, the developed scanner successfully upgrades the existing tomography setup in the industry. Here, single pencil beam gamma ray computed tomography is already used to study hydraulics in gas–liquid contactors, with inner diameters of up to 440 mm. We demonstrate the functionality of the new system for radiographic and computed tomographic scans of DN110 and DN440 columns that are operated at varying iso-hexane/nitrogen liquid–gas flow rates. Full article

(This article belongs to the Special Issue Tomographic and Multi-Dimensional Sensors)

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29 pages, 12815 KiB

Open AccessArticle

Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks

by Nishant Kumar, Lukas Krause, Thomas Wondrak, Sven Eckert, Kerstin Eckert and Stefan Gumhold

Sensors 2024, 24(4), 1213; https://doi.org/10.3390/s24041213 - 14 Feb 2024

Viewed by 1499

Abstract

Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells. Additionally, these gas bubbles induce alterations in the conductivity of the electrolyte, leading to corresponding fluctuations in the magnetic flux density outside of the electrolysis cell, which can be measured by externally placed magnetic sensors. By solving the inverse problem of the Biot–Savart Law, we can estimate the conductivity distribution as well as the void fraction within the cell. In this work, we study different approaches to solve the inverse problem including Invertible Neural Networks (INNs) and Tikhonov regularization. Our experiments demonstrate that INNs are much more robust to solving the inverse problem than Tikhonov regularization when the level of noise in the magnetic flux density measurements is not known or changes over space and time. Full article

(This article belongs to the Special Issue Tomographic and Multi-Dimensional Sensors)

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