Resistor Network for Forward Calculation in Electrical Resistance Tomography of Carbon Fiber-Reinforced Composites

Carbon fiber-reinforced composites (CFRCs) exhibit the characteristic of coupling resistance and damage; therefore, electrical resistance tomography (ERT) can be used to image in situ damage. However, there is a lack of research on the development of forward calculation for CFRCs anisotropy in ERT. In this article, a resistor network (RN) is proposed as an ERT forward calculation method based on the structure and conductive properties of CFRCs. The CFRCs is equivalent to a RN through segmentation and projection. Compared with the finite element method (FEM), the forward calculation speed using the RN method is increased by approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$250\times $ </tex-math></inline-formula>. Through convergence analysis, RN can effectively detect single-point areas of damage with a rate larger than 1%. Using the network calculation mode, the RN method can efficiently set anisotropic conductivity according to material properties to obtain realistic imaging effects. In this study, through the experimental verification of CFRPs and C/SiC, ERT combined with RN could effectively detect damage information, increasing the overall imaging speed by 100%.

Authors

Institutions

Publication Details

Journal
IEEE Sensors Journal
Published
2024-09-17
DOI
https://doi.org/10.1109/jsen.2024.3458069
Citations
2
Primary Topic
Smart Materials for Construction
Type
article
Field-Weighted Citation Impact
0.27
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Resistor Network for Forward Calculation in Electrical Resistance Tomography of Carbon Fiber-Reinforced Composites

Xin Pan, Qiang Yang, Zhi Wang, Bo Gao et al.
2 citations
IEEE Sensors Journal
Smart Materials for Construction
0.27
article

Resistor Network for Forward Calculation in Electrical Resistance Tomography of Carbon Fiber-Reinforced Composites

Xin Pan, Qiang Yang, Zhi Wang, Bo Gao, Songhe Meng, Zheng-Yuan Shen
article en
2 citations

Abstract

Carbon fiber-reinforced composites (CFRCs) exhibit the characteristic of coupling resistance and damage; therefore, electrical resistance tomography (ERT) can be used to image in situ damage. However, there is a lack of research on the development of forward calculation for CFRCs anisotropy in ERT. In this article, a resistor network (RN) is proposed as an ERT forward calculation method based on the structure and conductive properties of CFRCs. The CFRCs is equivalent to a RN through segmentation and projection. Compared with the finite element method (FEM), the forward calculation speed using the RN method is increased by approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$250\times $ </tex-math></inline-formula>. Through convergence analysis, RN can effectively detect single-point areas of damage with a rate larger than 1%. Using the network calculation mode, the RN method can efficiently set anisotropic conductivity according to material properties to obtain realistic imaging effects. In this study, through the experimental verification of CFRPs and C/SiC, ERT combined with RN could effectively detect damage information, increasing the overall imaging speed by 100%.

IEEE Sensors JournalVol. 24(22)
Harbin Institute of Technology (CN)
Openalex Percentile: Top 53%
Smart Materials for Construction
0.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.