Water retention properties of a fused deposition modeling based 3D printed polylactic acid vessel

• Main Authors: Muhammad Nur Farhan, Saniman, Nadzir Akif, Dzulkifli, Khairul Anuar, Abd Wahid, Wan Mansor, Wan Muhamad, Khairul Azhar, Mohamad, Erny Afiza, Alias, Jamilah, Mohd Shariff
• Format: Book Section
• Language: English
• Published: Sprinter. Champ 2022
• Subjects: TJ Mechanical engineering and machinery; TS Manufactures
• Online Access: http://umpir.ump.edu.my/id/eprint/34981/1/1.Advanced%20Maritime%20Technologies%20and%20Applications%20%282%29.pdf; http://umpir.ump.edu.my/id/eprint/34981/; http://10.1007/978-3-030-89992-9_27; https://doi.org/10.1007/978-3-030-89992-9_27

The applications of fused deposition modelling (FDM) based 3D printing have gone beyond merely simple prototypes to where functionalities are expected. One of such functionalities is the water retention properties, especially for fluid handling products, either completely waterproof or deliberately porous. Issues arise especially in determining crucial parameters and their optimization to achieve the desired water retention properties. This study established the relationship among printing parameters (layer thickness and wall thickness) and water temperature with leakage flow rate. A series of 3D printed polylactic acid (PLA) vessels were fabricated at various layer height and wall thickness. Then, the volumetric loss of water at various temperatures was measured, elapsed time was recorded, and the leakage flow rate was calculated for each 3D printed vessel. It has been found that the leakage flow rate decreased when layer height decreased, wall thickness increased, and water temperature decreased. Based on multilinear regression analysis, the magnitude of influence for the layer height was the highest, which could reach at a point where variation in wall thickness and water temperature had no effect. A regression model having 81.27% fitness that provided a quantitative relationship among all parameters had also been obtained. ANOVA analysis revealed that all parameters were statistically significant in optimizing as well as predicting the value of the leakage flow rate.