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Finite element modeling of the electrical impedance tomography technique driven by machine learning

Mohamed Elkhodbia Orcid Logo, Imad Barsoum Orcid Logo, Feras Korkees Orcid Logo, Shrinivas Bojanampati

Finite Elements in Analysis and Design, Volume: 223, Start page: 103988

Swansea University Author: Feras Korkees Orcid Logo

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DOI (Published version): 10.1016/j.finel.2023.103988

Abstract

To create a human-like skin for a robotic application, current touch sensor technologies have a few drawbacks. Electrical Impedance Tomography (EIT) is a candidate for this application due to its applicability over complex geometries; nevertheless, it has accuracy concerns. This study employs artifi...

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Published in: Finite Elements in Analysis and Design
ISSN: 0168-874X
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63673
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spelling v2 63673 2023-06-20 Finite element modeling of the electrical impedance tomography technique driven by machine learning 4d34f40e38537261da3ad49a0dd2be09 0000-0002-5131-6027 Feras Korkees Feras Korkees true false 2023-06-20 MTLS To create a human-like skin for a robotic application, current touch sensor technologies have a few drawbacks. Electrical Impedance Tomography (EIT) is a candidate for this application due to its applicability over complex geometries; nevertheless, it has accuracy concerns. This study employs artificial neural networks (ANNs) to investigate the accuracy and capability of EIT-based touch sensors. A finite element (FE) model is utilized to solve the forward EIT problem while simultaneously determining the system’s comprehensive mechanical response. The FE model is comprised of a polyurethane (PU) foam domain, a conductive spray layer and a set of sixteen electrodes. To replicate the process of touching the sensor body, a punch of varying diameters and touch forces is utilized. The mechanical response of the sensor body is modeled using the hyperfoam material model calibrated through experimental uniaxial and shear test data, while the electric conductivity of the sprayed skin surface is obtained experimentally as function of applied strain. The viscoelastic behavior of the PU foam material is also obtained experimentally. These experimental data were implemented in the FE model through user subroutines to model the mechanical and electrical properties of the sensor in the EIT forward problem. The traditional EIT inverse problem image reconstruction was replaced utilizing ANNs as an alternative to extract mechanics based parameters. The ANNs were created to predict the spatial coordinates of the touch point, and they were proven to be extremely accurate. Using the EIT voltage readings as input, the ANNs were utilized to forecast the system’s mechanical behavior such as contact pressure, contact area, indentation depth, and touching force. Journal Article Finite Elements in Analysis and Design 223 103988 Elsevier BV 0168-874X 1 10 2023 2023-10-01 10.1016/j.finel.2023.103988 http://dx.doi.org/10.1016/j.finel.2023.103988 COLLEGE NANME Materials Science and Engineering COLLEGE CODE MTLS Swansea University The authors acknowledge the financial support provided by Khalifa University, United Arab Emirates. 2023-10-05T13:34:11.8130499 2023-06-20T12:27:10.4984770 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Mohamed Elkhodbia 0000-0002-3325-9882 1 Imad Barsoum 0000-0002-9438-9648 2 Feras Korkees 0000-0002-5131-6027 3 Shrinivas Bojanampati 4 63673__28686__7cfa1bf1914d4d6b9d7be2c6386429b4.pdf 63673.Accepted manuscript.pdf 2023-10-03T11:49:45.1047338 Output 5920611 application/pdf Accepted Manuscript true Distributed under the terms of a Creative Commons CC BY-NC-ND licence. true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Finite element modeling of the electrical impedance tomography technique driven by machine learning
spellingShingle Finite element modeling of the electrical impedance tomography technique driven by machine learning
Feras Korkees
title_short Finite element modeling of the electrical impedance tomography technique driven by machine learning
title_full Finite element modeling of the electrical impedance tomography technique driven by machine learning
title_fullStr Finite element modeling of the electrical impedance tomography technique driven by machine learning
title_full_unstemmed Finite element modeling of the electrical impedance tomography technique driven by machine learning
title_sort Finite element modeling of the electrical impedance tomography technique driven by machine learning
author_id_str_mv 4d34f40e38537261da3ad49a0dd2be09
author_id_fullname_str_mv 4d34f40e38537261da3ad49a0dd2be09_***_Feras Korkees
author Feras Korkees
author2 Mohamed Elkhodbia
Imad Barsoum
Feras Korkees
Shrinivas Bojanampati
format Journal article
container_title Finite Elements in Analysis and Design
container_volume 223
container_start_page 103988
publishDate 2023
institution Swansea University
issn 0168-874X
doi_str_mv 10.1016/j.finel.2023.103988
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
url http://dx.doi.org/10.1016/j.finel.2023.103988
document_store_str 1
active_str 0
description To create a human-like skin for a robotic application, current touch sensor technologies have a few drawbacks. Electrical Impedance Tomography (EIT) is a candidate for this application due to its applicability over complex geometries; nevertheless, it has accuracy concerns. This study employs artificial neural networks (ANNs) to investigate the accuracy and capability of EIT-based touch sensors. A finite element (FE) model is utilized to solve the forward EIT problem while simultaneously determining the system’s comprehensive mechanical response. The FE model is comprised of a polyurethane (PU) foam domain, a conductive spray layer and a set of sixteen electrodes. To replicate the process of touching the sensor body, a punch of varying diameters and touch forces is utilized. The mechanical response of the sensor body is modeled using the hyperfoam material model calibrated through experimental uniaxial and shear test data, while the electric conductivity of the sprayed skin surface is obtained experimentally as function of applied strain. The viscoelastic behavior of the PU foam material is also obtained experimentally. These experimental data were implemented in the FE model through user subroutines to model the mechanical and electrical properties of the sensor in the EIT forward problem. The traditional EIT inverse problem image reconstruction was replaced utilizing ANNs as an alternative to extract mechanics based parameters. The ANNs were created to predict the spatial coordinates of the touch point, and they were proven to be extremely accurate. Using the EIT voltage readings as input, the ANNs were utilized to forecast the system’s mechanical behavior such as contact pressure, contact area, indentation depth, and touching force.
published_date 2023-10-01T13:34:13Z
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