Fused deposition modeling is a widely adopted additive manufacturing technique due to its affordability and ability to produce complex geometries. However, selecting the fused deposition modeling process parameters to achieve multi-objective responses remains challenging due to their conflicting behaviors. For this purpose, a novel hybrid framework integrating Grey Relational Analysis and Particle Swarm Optimization, guided by Taguchi method, focuses on improving the tensile strength of FDM 3D polylactic acid materials. Raster angle, layer height, nozzle temperature, print speed, and bed temperature were considered as the input parameters. A statistically validated regression model accurately predicts Grey Relational Grades, enabling efficient multi-objective optimization of FDM process parameters. Statistical analysis was performed to identify the significance and influence of key input parameters. This study finds better prediction accuracy with Taguchi-Grey Relational Analysis-Particle Swarm Optimization. This method improved the Grey Relational Grade by 37% over Taguchi-Grey Relational Analysis prediction and 35.8% compared to the initial optimal parameters derived from experimental parameters. The maximum ultimate tensile strength and percentage elongation were achieved under the following conditions: a raster angle of 0°, nozzle temperature of 210 °C, layer height of 0.1 mm, print speed of 60 mm/s, and bed temperature of 60 °C. The optimal parameters of the hybrid Taguchi-GRA-PSO method, validated against experimental data, achieved experimentally 74.5% of the polylactic acid filament’s ultimate tensile strength and 82.5% of the percentage elongation. The effects of input parameters on macrostructure, microstructure, and dynamic mechanical analysis of 3D-printed PLA material are investigated. The results of this study provide practical insights for industries employing FDM 3D printing processes with polylactic acid materials.
Boulahem et al. (Fri,) studied this question.
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