Influence of Slicing Strategy on FFF Parts Dimensional Deviations

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Abstract:

Dimensional accuracy is critical in industrial applications and can limit the use of Fused Filament Fabrication FFF. The printing steps required in material extrusion: pre-processing, processing, and post-processing influence dimensions. While processing and post-processing steps lead to inaccuracies due to machine limitations, dimensional errors from the pre-processing step are motivated by inadequate representations and mathematical approaches. This work studies if the slicing approach can contribute to dimensional deviations. It is considered a pyramid sample with four different wall angles to check this hypothesis. Next, its CAD model is sliced using three approaches: inclusive, middle, and exclusive. The resulting gcodes were compared against the theoretical pyramid sections to quantify deviations. It is worth mentioning that each slicing approach shows a different deviation curve along the printing direction and the wall angle. These results highlight the slicing strategy as a potential source of dimensional inaccuracies. Finally, three sample cases (one for each slicing strategy) are printed, measured, and compared using a 3D scanner and conventional measurements to support the numerical examples.

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57-63

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October 2023

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© 2023 Trans Tech Publications Ltd. All Rights Reserved

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[1] Richard Hague, Philip Dickens, and Neil Hopkinson. Rapid manufacturing: an industrial revolution for the digital age. John Wiley & Sons, 2006.

Google Scholar

[2] Rafiq Noorani. 3D printing: technology, applications, and selection. CRC Press, 2017.

Google Scholar

[3] ASTM ISO. Iso/astm 52900: 2015 additive manufacturing-general principles-terminology, 2015.

DOI: 10.31030/2631641

Google Scholar

[4] Gustavo Medina-Sanchez, Rubén Dorado-Vicente, Eloísa Torres-Jiménez, and Rafael LópezGarcía. Build time estimation for fused filament fabrication via average printing speed. Materials, 12(23):3982, 2019.

DOI: 10.3390/ma12233982

Google Scholar

[5] Marco Attene, Marco Livesu, Sylvain Lefebvre, Thomas Funkhouser, Szymon Rusinkiewicz, Stefano Ellero, Jonàs Martínez, and Amit Haim Bermano. Design, representations, and processing for additive manufacturing. Synthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography, and Imaging, 10(2):1-146, 2018.

DOI: 10.1007/978-3-031-02596-9

Google Scholar

[6] Huachao Mao, Tsz-Ho Kwok, Yong Chen, and Charlie C.L. Wang. Adaptive slicing based on efficient profile analysis. Computer-Aided Design, 107:89-101, 2019.

DOI: 10.1016/j.cad.2018.09.006

Google Scholar

[7] Jesús Miguel Chacón, Javier Sanchez-Reyes, Javier Vallejo, and Pedro José Núñez. G-code generation in a nurbs workflow for precise additive manufacturing. Rapid Prototyping Journal, 28(11):65-76, 2022.

DOI: 10.1108/rpj-09-2021-0254

Google Scholar

[8] William Oropallo and Les A Piegl. Ten challenges in 3d printing. Engineering with Computers, 32:135-148, 2016.

DOI: 10.1007/s00366-015-0407-0

Google Scholar

[9] Wolfram mathematica: Modern technical computation. https://www.wolfram.com/mathematica/. Accessed on March 3, 2023.

Google Scholar

[10] Cloudcompare - open source project. https://www.danielgm.net/cc/. Accessed on March 3, 2023.

Google Scholar