No abstract

Flax fibers were used to process unidirectional composites by two different methods. Their mechanical properties obtained by tensile testing are discussed with respect to the properties of the fibers and those of the matrix (unsatured polyester). The similarity of the tensile curves of the composites and of the elementary fibers is attributed to the good adhesion of the fibers with the matrix. Moreover, as there is almost a linear evolution of the composite properties with the fiber volume fraction, these properties are used to estimate those of the real reinforcement material, that is, the flax bundles: the calculations lead to a fiber strength of 500800 MPa and a fiber modulus of roughly 30 GPa, which is half the values obtained by tensile testing elementary fibers. These data may be helpful when trying to model the deformation behavior of flax fiber-reinforced composites.

No abstract

Liquid composite molding processes are widely accepted in the aeronautic industry to manufacture large and complex structural parts. In spite of their cost-effectiveness, void defects created during the manufacturing process are a major issue of these processing techniques because they have detrimental effects on the mechanical performance. The reliable modeling is still a difficult task and experimental observations are usually adopted for the analysis of void formation mechanism, however, because many different physics are simultaneously involved during the mold filling process and the resin curing process. The complexity of the void formation physics implies the need for an in situ measurement of void formation not in the final part but in the mold filling procedure during the manufacturing process to better understand the void mechanism. In this regard, we present a sensor system measuring the electric conductivity for the in situ monitoring of void formation during the mold filling process. We also propose a theoretical model to predict void formation in a quantitative way with the properties of the resin and the fiber reinforcement. The model prediction is compared with the experimental data obtained by the sensor system to validate the model.