Characterization of continuous carbon fibre reinforced 3D printed polymer composites with varying fibre volume fractions
Source abstract
Abstract
Fused deposition modelling (FDM) is one of the most popular additive manufacturing (AM) technique which is used to investigate the elastic properties of 3D printed polyamide-based polymer composites structures. The aim of this work is to study the mechanical properties of continuous carbon fibre reinforced polyamide polymer composite samples using tensile and flexural testing by varying the fibre volume contents with applying pressure, temperature and holding the samples for 60 minutes in the platen press. The results showed that the strength and stiffness increased with the increase in fibre volume content (fraction). Hot pressed samples exhibited the increase in tensile strength by about 27 % and elastic modulus by 11 % because of increasing the fibre volume fraction from 29 % to 35%. Synergetic effect of both short and continuous carbon fibre was also studied, and it was observed that the tensile properties were higher for the samples reinforced with short and continuous fibre than only continuous fibre polymer composites. Effects of voids on 3D printed continuous carbon fibre-reinforced polymer composites were quantified. A microstructure study of the 3D printed polymer composites was carried out using scanning electron microscope (SEM). Following SEM analysis on the tested specimens, it was observed that there was a strong correlation between the mechanical properties and the microstructure. Fibre volume fraction was measured using acid digestion method to determine the amount of fibre contents before and after hot pressing (compaction). From Micro- Computed Tomography (µCT) it was confirmed that hot pressing reduced the void content which in return increased the strength and modulus.
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Research context
OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Bone Tissue Engineering Materials · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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