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Article | Green Intelligent Systems and Applications | Volume 4 - Issue 2 - 2024 | 89-97 | https://doi.org/10.53623/gisa.v4i2.522
© 2024 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Artificial Neural Network for Benchmarking the Dimensional Accuracy of the PLA Fused Flament Fabrication Process

Author(s): Kevin Stephen Setiawan 1 , Irvantara Pradmaputra Tanaji 1 , Ari Permana 1 , Hafizh Naufaly Akbar 1 , Dhonadio Aurell Azhar Prihatmaja 1 , Nur Mayke Eka Normasari 1 , Achmad Pratama Rifai 1 , Panca Dewi Pamungkasari 2
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1 Departement of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
2 Faculty of Computer Information Technology, Universitas Nasional, Jakarta, Indonesia

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Green Intelligent Systems and Applications

Volume 4 - Issue 2 - 2024

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Fused Deposition Modeling (FDM) is an additive manufacturing technique that uses a 3D printer to extrude molten filament through a nozzle, which moves along the X, Y, and Z axes to create parts with the desired geometry. FDM offers numerous advantages, especially for producing parts with complex shapes, due to its ability to enable rapid and cost-effective manufacturing compared to traditional methods. This study implemented an Artificial Neural Network (ANN) to optimize process parameters aimed at minimizing dimensional inaccuracies in the FDM process. Key parameters considered for optimization included the number of shells, infill percentage, and nozzle temperature. The ANN utilized three algorithms: Scaled Conjugate Gradient, Bayesian Regularization, and Levenberg-Marquardt. Model performance was evaluated based on dimensional deviations along the X and Y axes, with a hidden layer of 25 neurons. Among the algorithms, Scaled Conjugate Gradient provided the most accurate results in minimizing dimensional errors.

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About this article

SUBMITTED: 11 October 2024
ACCEPTED: 04 November 2024
PUBLISHED: 17 November 2024
SUBMITTED to ACCEPTED: 25 days
DOI: https://doi.org/10.53623/gisa.v4i2.522

Cite this article
Setiawan, K. S. ., Tanaji, I. P., Permana, A. ., Akbar, H. N. ., Prihatmaja, D. A. A. ., Normasari, N. M. E. ., Rifai, A. P., & Pamungkasari, P. D. . (2024). Artificial Neural Network for Benchmarking the Dimensional Accuracy of the PLA Fused Flament Fabrication Process. Green Intelligent Systems and Applications, 4(2), 89–97. https://doi.org/10.53623/gisa.v4i2.522
Keywords
Fused Deposition Modeling Artificial Neural Network Scaled Conjugate Gradient Bayesian Regularization Levenberg-Marquardt
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