journal article·2022-04

Impact and strain monitoring in glass fiber reinforced epoxy laminates with embedded quantum resistive sensors (QRSs)

A. Lemartinel, M. Castro, O. Fouche, J.C. De Luca, J.F. Feller

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Abstract

International audience ; The use of composite materials in large structures such as wind turbine blades, proposed as a renewable energy solution, is currently increasing. Due to their expected lifetime, they may be subjected to a variety of punctual damaging events, such as impacts. The extent of damage caused by impacts can range from weak (rain) to locally destructive (birds). Detecting and locating these impacts could help assess the health of the part, monitor its state and could lead to a better prediction of its ultimate failure, thus reducing maintenance cost, within a broader framework of structural health monitoring. Here, we studied the use of Quantum Resistive Sensors (QRSs) for the detection and localisation of impacts that could be whether hail, bird or maintenance tool impacts occurring on an epoxy-glass fibre laminate. At an acquisition frequency of 4800 Hz, the electrical behaviour of QRSs was found to be simultaneous with the impact load. The use of multiple sensors within and between plies further evidenced that the change in resistance is proportional to the impact energy and could help to locate the impact area. The effect of repeated impacts on the electrical properties of the QRSs was then studied. With the accumulation of impacts that have reduced Young's modulus of the sample, both the resistance at rest and the amplitude of the response during dynamic cycle increased. Therefore, QRSs could provide an in situ probe in composites to detect, localize, quantify the energy of an impact to anticipate the resulting drops in mechanical properties of the composite.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Smart Materials for Construction · Thermography and Photoacoustic Techniques · Electrical Fault Detection and Protection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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