An experimental study on the incorporation of carbon nanotubes into resin transfer molded composites

Fiber reinforced composites can be engineered to present superior mechanical, thermal and electrical properties if primary constituents are integrated with nano phase structures. The preferable integration methodology is such that it should be easily applicable, up scalable and of low cost for industrial needs. There are numerous studies on literature based on mainly four different manufacturing methods, namely, in-resin infusion,CVD growth, interlyer placement and electrophoretic deposition. Each of these methods has distinct drawbacks such as dispersion, viscosity and alignment etc. in integrating nano phase structures (i.e., carbon nanotubes, nano fibers of various polymers, or graphene) into fiber reinforced composites. The integration of nano structures into traditional polymeric composites by means of these techniques at large scale still remains as a challenge ahead. Therefore, we have developed a new approach to circumvent associated up-scaling and manufacturing issues wherein carbon nanotubes are incorporated into fiber reinforced composites manufactured by Resin Transfer Molding (RTM) method through electro-spraying and electro-spinning processes. This study covers the incorporation of carbon nanotubes (CNT) with and without use of a surface agent and incorporation of epoxy compatible CNT grafted nanofibrous interlayer. Design and operation of experimental system focusing on the dispersion and alignment of carbon nanotubes and pertinent mechanical test results are presented

Fatigue monitoring of glass fiber reinforced composite using fiber Bragg grating

Glass fiber reinforced composite (GFRC) are widely used in all load bearing industrial applications. It is therefore critical to understand the fatigue characteristic of GFRC to improve the material characteristics of these composites and to prevent any untoward accidents. In this paper we describe the preliminary results of fatigue test on GFRC fabricated using an in-house laboratory scale resin transfer molding system. The fatigue tests are done for different load conditions – tension compression and reverse bending. Fiber Bragg grating (FBG) is embedded into the composite at the manufacturing step for monitoring the strain. The preliminary results of these experiments are presented

Study of local and transient buckling in glass fiber reinforced composite using fiber bragg grating

Many models encountered in computer science remain intractable because of their tremendous complexity. Among them, the numerical modeling of manufacturing processes involving several characteristic times is a challenging issue. Classical incremental methods often fail for solving efficiently such transient models. In that sense model reduction based simulation appears to be a very promising alternative. Multidimensional parametric models can be solved within the context of the Proper Generalized Decomposition (PGD). It opens new horizons regarding time parallelization. Indeed, by no more considering the initial condition of a transient problem as a static input data but as an extra- coordinate similarly to space and time, we demonstrate that it is possible to parallelize efficiently the computation and even reach real-time in some cases

Prediction of fatigue response of composite structures by monitoring the strain energy release rate with embedded fiber Bragg gratings

Composite materials are becoming increasingly more valuable due to their high specific strength and stiffness. Currently, most components are operated for a number of service cycles and then replaced regardless of their actual condition. Embedded fiber Bragg gratings are under investigation for monitoring these components in real time and estimating their remaining life. This article presents research conducted on a novel technique for prediction of the remaining life of composites under fatigue loading using embedded fiber Bragg grating sensors. A prediction is made of the remaining life at every cycle based on data collected from the sensors and the previous loading history