Sandwich composites are vulnerable to low-velocity impacts that they are mainly exposed to. But non-destructive testing for sandwich composite is troublesome due to its discrete and inhomogeneous nature. This study investigates the damage severity of the non-conductive foam core sandwich composite using the electrical resistance change of Carbon fiber-reinforced plastics skins under impact. Four severity levels are paired to form 4C2 binary classifications, Single Linear Discriminant Analysis (LDA), using electrical resistance change as an input. By combining six Single LDAs’ probabilistic results into one system, this system can represent the specimen’s current state intuitively and diagnose the damage severity synthetically.
Electromechanical properties of carbon fibers enable non-destructive evaluation (NDE) of carbon-fiber-reinforced plastic (CFRP) structures by monitoring electrical resistance in real-time. This NDE technique is named as ‘self-sensing’, as it employs the material’s intrinsic features like human nerves. This technique was applied to evaluate the size of damage in CFRP samples. Electrical resistance measured in real-time during machining increasing size of concentric circles was analyzed, and polynomic correlations were identified. To ensure reliability and take uncertainties account, probability-based tools, Markov chain Monte Carlo and Bayesian algorithm, were applied. The potential applicability of the established system to repeated impact loads, considering damage progression due to unexpected strikes in real applications, was also verified.
Fiber-reinforced plastic (FRP) is a material used to reinforce civil engineering structures. Carbon-fiber-reinforced plastic (CFRP) is composed of conductive reinforcing filler and dielectric matrix. By utilizing the internal structure of CFRP and the difference in electrical conductivity of the components, carbon fiber (CF) and resin matrix can serve as a structural electrode and a frictional material, respectively. Electrostatic charge is generated by friction between resin covering CF and external materials. It induces an alternating current through CF according to the distance change between charged layers. Then, CFRP can be applied as a structural sensor using the triboelectric effect. In this research, we identified the triboelectric effect occurring on the surface of the composite and the electrostatic induction phenomenon occurring inside the composite. In addition, the voltage signal changes according to the movement of external materials. By identifying the effects that occur in composite materials, we have confirmed the possibility of realizing a smart civil engineering structure that can detect touch by itself.
Carbon fiber (CF) holds structural self-sensing capability using its electrical resistance so that electromechanical behavior of carbon fiber reinforced plastic (CFRP) was investigated for using CF and carbon-glass hybrid fiber (CGHF). CGHF contains CF in either warp or weft, whereas glass fiber is perpendicularly woven in the other. Electrical resistance of CF monofilament whose diameter is 8 μm was increased when tensile strain was applied in fiber direction, which is called piezoresistive effect. When CF is gathered into a bundle, similar piezoresistive effect was observed. Moreover, distance change between the adjacent CF also led to the resistance change, because the number of electrical contacts can be differed with respect to the tow gap. We call this phenomenon “inter-tow interaction.” Another discriminative electromechanical contact is “inter-ply interaction” which has electromechanical contact between adjacent plies. Likewise, several electromechanical factors hold structural self-sensing capability. Therefore, real-time non-destructive evaluation (NDE) and structural health monitoring (SHM) can be realized with carbon fiber. The CF for the self-sensing can be constituted in various forms in a polymer matrix such as a plain-woven fabric, a uni-directional fabric and a grid. The self-sensing CF grid can be a compromised arrangement between the sensing performance and the material cost. In addition, the self-sensing algorithms of CFs can be comprehended by electrically equivalent circuit models. Reversely, the sensor design can be aided by the equivalent model which contains the aforementioned interactions.
Health state monitoring and prognostics and management of composite were investigated with piezoresistivity data based on the electromechanical behavior of carbon fibers during dual cantilvever bending testing. Crack length in real-time and remaining crack length were calculated with measured electrical resistance. Prediction of crack length was estimated based on prediction result of electromechanical behavior. This research indicated optimized in situ diagnosis and prognosis of carbon fiber reinforced composites with self-sensing data. Self-sensing capability of self-sensing data using electrical resistance was investigated which is applicable to both SHM and PHM.
Electromechanical behavior of carbon-fiber-reinforced plastics (CFRPs) was investigated by monitoring the electrical resistance changes with respect to mechanical loading to utilize its self-sensing capability for real-time non-destructive evaluation (NDE). Electrical resistance changed as mechanical deformations occurred in CFRPs. CFRP consists of polymer matrix and carbon fiber consisting of several thousands of carbon fiber monofilaments. The intrinsic piezoresistive behavior of a carbon fiber monofilament was characterized by an increase in electrical resistance when subjected to tensile elongation. A carbon fiber tow, essentially a bundle of monofilaments, also displayed a similar electromechanical behavior. In addition, the electrical resistance was affected by the interaction between adjacent tows and plies, known as “inter-tow” and “inter-ply” interactions, respectively. These interactions can be modeled as electrically equivalent circuits with variable electrical resistors. The developed model aids in the design of self-sensing CFRPs, which holds real-time NDE ability. Variable electrical resistors were parameterized by both empirical results and numerical analysis, decoupling each factor containing the stacking sequence as well as orientation of carbon fiber plies. The proof-of-concept of self-sensing CFRP was demonstrated using a 3D-printed miniaturized bridge. CFRP strips were attached under the bridge, and electrical resistances were monitored real-time with respect to the deflection. The acquired resistance changes were converted using the in-house developed algorithm, and deflections were calculated. It was shown that the proposed method can detect both the locations and magnitudes of deflections in the bridge real-time even when moving loads are applied.
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