Modeling the producibility of 3D printing in polylactic acid using artificial neural networks and fused filament fabrication

Meiabadi, M.S., Moradi, M., Karamimoghadam, M., Ardabili, S., Bodaghi, M. ORCID: 0000-0002-0707-944X, Shokri, M. and Mosavi, A.H., 2021. Modeling the producibility of 3D printing in polylactic acid using artificial neural networks and fused filament fabrication. Polymers, 13 (19): 3219. ISSN 2073-4360

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Abstract

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.

Item Type: Journal article
Publication Title: Polymers
Creators: Meiabadi, M.S., Moradi, M., Karamimoghadam, M., Ardabili, S., Bodaghi, M., Shokri, M. and Mosavi, A.H.
Publisher: MDPI AG
Date: 23 September 2021
Volume: 13
Number: 19
ISSN: 2073-4360
Identifiers:
NumberType
10.3390/polym13193219DOI
1473893Other
Rights: © 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions: Schools > School of Science and Technology
Record created by: Lee Houghton
Date Added: 27 Sep 2021 09:55
Last Modified: 27 Sep 2021 09:55
URI: http://irep.ntu.ac.uk/id/eprint/44257

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