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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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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 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.
College: Faculty of Science and Engineering
Funders: The authors acknowledge the financial support provided by Khalifa University, United Arab Emirates.
Start Page: 103988

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