Please use this identifier to cite or link to this item:
Title: Modeling the stress and resistance relaxation of conductive composites-coated fabric strain sensors
Authors: Wang, X
Yang, B 
Li, Q
Wang, F 
Tao, XM 
Issue Date: Mar-2021
Source: Composites science and technology, 1 Mar. 2021, v. 204, 108645
Abstract: Electrical relaxation of flexible sensors using the conductive polymer composites as sensing materials has been constantly reported as major obstacle for accurate measurement, yet still roughly characterized by mechanical relaxation rather than an effective underlying mechanism. In this work, fabric strain sensors based on carbon-particle-filled conductive polymer and knitted fabric substrate were studied. A serial mechanical model of the sensor was established according to its structure, and then extended to an electromechanical model by introducing strain-resistance properties for mechanical elements. Methods were elaborated on extracting the mechanical, electrical and status parameters of the model. Tests were conducted on 5 randomly-chosen samples. The model was firstly determined for each sample using proposed methods and then implemented to predict resistance response during relaxations. Results show that the relative mean error of the predicted resistance was only 0.2%, with an averaged determination of fit 0.9230. The correlation between predicted and measured resistance was observed 0.9783 on average. Conclusion can be drawn that the model is effective to characterize the sensing mechanism and resistance relaxation of the fabric strain sensors.
Keywords: Fabrics/textiles
Polymer-matrix composites
Electro-mechanical behavior
Material modeling
Stress relaxation
Publisher: Pergamon Press
Journal: Composites science and technology 
ISSN: 0266-3538
EISSN: 1879-1050
DOI: 10.1016/j.compscitech.2021.108645
Appears in Collections:Journal/Magazine Article

Open Access Information
Status embargoed access
Embargo End Date 2023-03-01
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Citations as of May 15, 2022


Citations as of May 12, 2022


Citations as of May 12, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.