Characteristics of carbon fiber based strain sensors for structural-health monitoring of textile-reinforced thermoplastic composites depending on the textile technological integration process

Abstract

The paper presents an effective online measurement technology for non-destructive and continuous monitoring of endless glass fiber reinforced thermoplastic (FRTP) composite structures using the numerous possibilities of textile process technologies for the design and one-step integration of one- and two-dimensional sensor structures based on piezo-resistive exPAN carbon filament yarn (CFY) in textile reinforced structures.The focus of this paper is directed at the analysis of such textile technological integrated sensors as an integral part of the textile reinforcement and their usability for in situ monitoring of local and/or global mechanical loading conditions in difficult or inaccessible areas within short distance of the structural load-bearing layers of FRTP's. If the adhesion of the carbon filament yarn sensor (CFYS) to the surrounding FRTP is sufficient enough, a change in geometry and thereby in resistivity of the carbon filaments can be measured by any mechanical load of the sensor-carrying FRTP component.The advantages and disadvantages of the tailored fiber placement (TFP), flat knitting and weaving textile processes for creating sensor structures will be shown, wherein the mentioned variety of interlacing techniques are used.The effects of the used textile manufacturing processes on the sensory properties are being compared. Furthermore, the effects of the integrated CFY on the mechanical properties of the basic structure are quantified. Sensor structures with minimal influence on the composite structure can be derived from it as well as sensors that enable a selective reinforcement of the composite structure. The textile integration provides a routing of one CFY through several layers of reinforcement. Thus, a damage-free and reproducible production of more complex, 3-dimensional, textile-based sensor networks is feasible in one step.