Mechanical performance evaluation of fiber composites equipped with In-Situ wireless sensor bodies

In modern day structural engineering, fiber-composites play a vital role for their capability for light-weight construction and high stiffness value. More and more applications are being developed in various industries ranging from science, architecture and engineering. These structures can also be equipped with multi-component sensor systems for different performance evaluations both during pre- and post-curing processes. In this work a novel method is developed to place wireless sensors inside the fiber reinforced composite system to enable multifunctionality without much trade-off in mechanical performance. Key objective here was to optimize the sensor shape to minimize stress accumulation and crack propagation around the sensor geometry inside the cured composite sample under stress. A finite element simulation model is developed for this purpose and a parametric model for the sensor geometry provided better insight into the force distribution along the fibers around the sensor element. Consequently, different testing sample combinations were prepared, for which, fibers were either cut or bend around the sensors and dielectric channels. Various composite samples with different shapes of sensor dummies were also experimentally tested to validate the computational results. CT scan models of post-cure samples before and after loading enabled in-depth understanding of fiber alignment that could cause disturbances in overall mechanical performance. The scan models also provided with sufficient information about unwanted porosity, and micro-crack growth inside the composite under loading, which turned out to be vital for establishing a reliable simulation model and improving parameters in manufacturing process. In the end, the goal of the work was to transport the know-how of such production unit from experimental and flexible manufacturing system like vacuum assisted resin infusion (VARI) to more sophisticated processing systems like prepreg manufacturing where all necessary information can be provided as inputs prior to the impregnation, thus removing error occurred due to manual handling.

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