Fabrication and analysis of integrated multifunctional MWCNTS sensors in glass fiber reinforced polymer composites
Source abstract
Abstract
Abstract Glass Fiber Reinforced Polymer (GFRP) composites have been widely used in advanced engineering applications, mainly in the automotive and aerospace industries. Due to its anisotropic nature, the damage and failure detection in composites under real-time loading are very complicated. Focusing on this problem, Multi-Walled Carbon Nanotubes (MWCNTs) based strain sensor for Structural Health Monitoring (SHM) of Fibre Reinforced Polymer (FRP) composites is proposed in this research. This study primarily aims to detect induced flexural strains and damages occurring in the composites in real-time for which glass fibers coated with carboxy and amide functionalized MWCNTs have been used as sensors. The interactions between MWCNTs and fiber surface were confirmed with FTIR. Carbon fiber sensors have been used for comparison. The sensors were embedded into GFRP composites which were subjected to three-point bending tests coupled with electrical resistance measurement to correlate the induced strain and damages with the fractional change in resistance across sensors. The electromechanical test results indicated that MWCNT coated sensors in GFRP composites show promising piezoresistive sensing characteristics with good cyclic reproducibility that is significant for in-situ strain monitoring and damage detection. Overall the highest strain sensitivity was observed with amide functionalized MWCNT sensors. The electrical response to temperature cycles showed a reproducible behavior with −8% relative resistance change within the temperature range of −10 °C to 80 °C which also signifies multifunctional characteristics of developed sensors.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Smart Materials for Construction · Ultrasonics and Acoustic Wave Propagation · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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