Shape measurement using a multicore optical fiber sensor with asymmetric dual cores

Abstract Shape measurement using multicore optical fiber sensors has attracted more attention in many fields due to the good consistency of the fiber cores. Three symmetrically arranged cores in a multicore fiber are usually used to reconstruct shapes by calculating the bending vectors, which will not be achieved when one of the cores is damaged or occupied in actual application. A shape measurement method using a multicore optical fiber sensor with asymmetric dual cores is proposed in this paper. Based on the analysis of the principle of shape reconstruction and the geometric relationship of the asymmetric dual cores in the multicore fiber sensor, the bending vector is decomposed. The mathematical expressions for the bending curvature and orientation of the asymmetric dual cores are derived. The two-dimensional (2D) and three-dimensional (3D) shape reconstruction results in both finite element modeling simulation and shape measurement experiments based on optical frequency domain reflectometry have shown that the proposed method using a multicore fiber sensor with asymmetric dual cores is able to achieve shape measurement; its performance is comparable and even equivalent to the traditional method that uses three symmetrically arranged cores. In the experiment, the maximum relative errors of 2D and 3D reconstructed shapes are 2.653% and 5.139%, respectively. The proposed method, which only needs asymmetric dual cores in the multicore optical fiber sensor for shape reconstruction, will be conducive to solving the limitations of multicore optical fiber sensors in shape measurement applications.

Authors

Institutions

Publication Details

Journal
Measurement Science and Technology
Published
2024-11-13
DOI
https://doi.org/10.1088/1361-6501/ad91d5
Citations
1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.35

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Shape measurement using a multicore optical fiber sensor with asymmetric dual cores

Qiufeng Shang, Jianjian Wang, Rui Tang
1 citations
Measurement Science and Technology
Industrial Vision Systems and Defect Detection
0.35
article

Shape measurement using a multicore optical fiber sensor with asymmetric dual cores

Qiufeng Shang, Jianjian Wang, Rui Tang
article en
1 citations

Abstract

Abstract Shape measurement using multicore optical fiber sensors has attracted more attention in many fields due to the good consistency of the fiber cores. Three symmetrically arranged cores in a multicore fiber are usually used to reconstruct shapes by calculating the bending vectors, which will not be achieved when one of the cores is damaged or occupied in actual application. A shape measurement method using a multicore optical fiber sensor with asymmetric dual cores is proposed in this paper. Based on the analysis of the principle of shape reconstruction and the geometric relationship of the asymmetric dual cores in the multicore fiber sensor, the bending vector is decomposed. The mathematical expressions for the bending curvature and orientation of the asymmetric dual cores are derived. The two-dimensional (2D) and three-dimensional (3D) shape reconstruction results in both finite element modeling simulation and shape measurement experiments based on optical frequency domain reflectometry have shown that the proposed method using a multicore fiber sensor with asymmetric dual cores is able to achieve shape measurement; its performance is comparable and even equivalent to the traditional method that uses three symmetrically arranged cores. In the experiment, the maximum relative errors of 2D and 3D reconstructed shapes are 2.653% and 5.139%, respectively. The proposed method, which only needs asymmetric dual cores in the multicore optical fiber sensor for shape reconstruction, will be conducive to solving the limitations of multicore optical fiber sensors in shape measurement applications.

Measurement Science and TechnologyVol. 36(1)
North China Electric Power University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 34%
Industrial Vision Systems and Defect Detection
0.35
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.