Journal article 376 views 105 downloads
Finite element modeling of the electrical impedance tomography technique driven by machine learning
Finite Elements in Analysis and Design, Volume: 223, Start page: 103988
Swansea University Author:
Feras Korkees
-
PDF | Accepted Manuscript
Distributed under the terms of a Creative Commons CC BY-NC-ND licence.
Download (5.65MB)
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...
Published in: | Finite Elements in Analysis and Design |
---|---|
ISSN: | 0168-874X |
Published: |
Elsevier BV
2023
|
Online Access: |
Check full text
|
URI: | https://cronfa.swan.ac.uk/Record/cronfa63673 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
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 |