Prediction of mechanical properties of flax/basalt short fiber hybrid polylactic acid composites based on Micro?CT

Multi?Scale Numerical Simulation of Flax/basalt short Fiber Hybrid Composites based on Micro?CT.Research on the performance design and prediction method of low carbon degradable composite materials is of great significance for realizing energy saving and emission reduction of automobile industry. In this study, plant short fiber reinforced polylactic acid composite (PFRP) was prepared by injection molding method and reinforced with basalt fiber to obtain flax/basalt short fiber hybrid reinforced composite (FBFRP). The mechanical properties of the composites were evaluated by quasi?static tensile/ bending test and scanning electron microscopy (SEM). It was observed that addition of basalt fiber further enhances the properties of the composite, and the properties of the composite are the best when the mass fraction of flax and basalt fiber is 30% and 9%. The fiber distribution inside FBFRP was analyzed by micro?computed tomography (Micro?CT) technology. Based on the real spatial characteristics of the fibers inside the materials, a microcosmic representative volume element (RVE) model of hybrid composites was established by writing a random adsorption algorithm script in Python. By defining the damage constitutive of each component, the macroscopic equivalent mechanical property parameters were obtained, and the influence of the interface thickness on the simulation results was studied. On this basis, the tensile and bending failure simulation of macro?composite materials is carried out, and the mechanical properties prediction of hybrid fiber composites based on multi?scale method is successfully realized.HighlightsUsing Micro?CT technique and random adsorption algorithm, the RVE model based on the real fiber distribution inside the composite was obtained.Through cross?scale simulation, the macroscopic equivalent mechanical properties of the composite were obtained.The effect of fiber?matrix interface thickness on the simulation results is considered.

» Publication Date: 20/11/2024

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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