Abstract
Fused Deposition Modeling (FDM) has become one of the most prevalent technologies in the field of additive manufacturing (AM). Nonetheless, polyethylene terephthalate glycol (PETG), a polymer renowned for its mechanical strength, has been significantly employed in 3D printing as an alternative to traditional materials. The incorporation of fibers renders PETG-based composites viable for a diverse range of applications. However, the tensile strength of PETG carbon fibre developed using FDM depends on multiple printing conditions. Therefore, this work aims to examine the impact of printing parameters (layer thickness, infill density, and printing speed) at three individual levels on the tensile and structural characteristics of PETG carbon fibre. A total of nine print runs were executed by manipulating the three parameters. Three samples were produced for each run to ensure consistency in the results. Tensile tests on the samples were carried out, and a scanning electron microscope (SEM) was used to examine the structure of the printed parts. Finally, L9 (33) orthogonal arrays were developed for the experiment. Following that, an analysis of variance (ANOVA) was conducted to determine the crucial factors and their ideal levels. The results showed that the infill density parameter significantly influenced the optimization of the tensile strength of PETG carbon fibre. The optimal value for this parameter was found to be 80%. Increasing the density of the infill improves the tensile strength by reducing the air gaps and decreasing deformation, leading to a solid and tightly packed structure. Thereafter, the most effective settings were identified as a layer thickness of 0.4 mm, an infill density of 80%, and a printing speed of 30 mm/s.
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Sukindar, N. A., Rahim, N. A. A. A., Yasir, A. S. H. M., Kamaruddin, S., Khata, M. T. A. H. M., Halim, N. F. H. A., … Aziz, A. A. A. (2024). OPTIMIZATION OF FDM 3D PRINTING PARAMETERS FOR TENSILE STRENGTH OF PETG CARBON FIBRE USING TAGUCHI METHOD. International Journal of Modern Manufacturing Technologies, 16(3 Special Issue), 143–152. https://doi.org/10.54684/ijmmt.2024.16.3.143
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