Experimental analysis of color influence on optimized FDM parameters for PLA using the Taguchi method
This study examines the influence of filament color on optimizing FDM process parameters for PLA parts using the Taguchi method. Parameters such as layer thickness, print speed, and printing temperature were varied to identify optimal settings for white and black PLA filaments. The results demonstra...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29332/2/0102303102025925302251.pdf http://eprints.utem.edu.my/id/eprint/29332/ https://www.ukm.my/jkukm/wp-content/uploads/2025/3706/13.pdf https://doi.org/10.17576/jkukm-2025-37(6)-13 |
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| Summary: | This study examines the influence of filament color on optimizing FDM process parameters for PLA parts using the Taguchi method. Parameters such as layer thickness, print speed, and printing temperature were varied to identify optimal settings for white and black PLA filaments. The results demonstrate that the optimal parameters vary based on color: for white PLA, the best configuration involves a layer thickness of 0.35 mm, print speed of 50 mm/s, and a printing temperature of 210°C. For black PLA, the same layer thickness and print speed are optimal, but the printing temperature is lower at 200°C. Layer thickness was identified as the most significant factor affecting tensile strength across both filament types. However, the ideal printing temperature depended on the color of the filament. Notably, white PLA exhibited higher tensile strength than black PLA, with an increase ranging from 1.33% to 15.54%, attributed to the thermal properties of color pigments. These findings highlight the critical role of filament color in determining mechanical performance during FDM printing. Incorporating filament color into the optimization of FDM parameters can enhance the quality, strength, and reliability of 3D-printed components. This research provides valuable insights for improving additive manufacturing outcomes across a range of applications. |
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