Research and Analysis of Carbon Fiber-Reinforced Polymer Prepreg Detection Based on Electromagnetic Coil Sensors

In response to the challenges posed by the complexity and potential hazards of traditional chemical methods for detecting the surface density of carbon fiber prepreg materials, this paper explores the use of eddy current testing principles. It establishes the relationship between coil impedance variation and the surface density of carbon fiber prepreg materials and designs a quadrupolar excitation eddy current detection probe. This probe can detect the surface density of both single-line and woven carbon fiber prepreg structures. The overall structure and dimensions of the designed quadrupolar probe were optimized using finite element simulation software. The results show that the number of coil turns significantly affects the sensor performance, with more turns leading to increased sensitivity. Moreover, with the same number of coil turns, smaller inner diameters and larger outer diameters of the coil enhance sensor sensitivity. A comprehensive comparison between unidirectional and woven carbon fiber models suggests that woven structures have superior electrical conductivity at identical excitation frequencies, while unidirectional models show more pronounced electrical anisotropy. These findings provide valuable insights for analyzing electrical properties, numerical simulations, and eddy current testing in composite materials.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2024-11-22
DOI
https://doi.org/10.3390/app142310807
Citations
1
Primary Topic
Non-Destructive Testing Techniques
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research and Analysis of Carbon Fiber-Reinforced Polymer Prepreg Detection Based on Electromagnetic Coil Sensors

Shouqi Cao, Sichang Zhang, Meiling Wang
1 citations
Applied Sciences
Non-Destructive Testing Techniques
0.21
article

Research and Analysis of Carbon Fiber-Reinforced Polymer Prepreg Detection Based on Electromagnetic Coil Sensors

Shouqi Cao, Sichang Zhang, Meiling Wang
article en
1 citations

Abstract

In response to the challenges posed by the complexity and potential hazards of traditional chemical methods for detecting the surface density of carbon fiber prepreg materials, this paper explores the use of eddy current testing principles. It establishes the relationship between coil impedance variation and the surface density of carbon fiber prepreg materials and designs a quadrupolar excitation eddy current detection probe. This probe can detect the surface density of both single-line and woven carbon fiber prepreg structures. The overall structure and dimensions of the designed quadrupolar probe were optimized using finite element simulation software. The results show that the number of coil turns significantly affects the sensor performance, with more turns leading to increased sensitivity. Moreover, with the same number of coil turns, smaller inner diameters and larger outer diameters of the coil enhance sensor sensitivity. A comprehensive comparison between unidirectional and woven carbon fiber models suggests that woven structures have superior electrical conductivity at identical excitation frequencies, while unidirectional models show more pronounced electrical anisotropy. These findings provide valuable insights for analyzing electrical properties, numerical simulations, and eddy current testing in composite materials.

Applied SciencesVol. 14(23)
Shanghai Ocean University (CN)
Openalex Percentile: Top 50%
Non-Destructive Testing Techniques
0.21
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.