Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

  • 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 thePolylactic 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.show moreshow less

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Metadaten
Document Type:Article
Author: Mohammad Saleh Meiabadi, Mahmoud Moradi, Mojtaba KaramimoghadamORCiD, Sina Ardabili, Mahdi BodaghiORCiD, Manouchehr ShokriORCiD, Amir H. Mosavi
DOI (Cite-Link):https://doi.org/10.3390/polym13193219Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20220110-45518Cite-Link
URL:https://www.mdpi.com/2073-4360/13/19/3219
Parent Title (English):polymers
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2022/01/10
Date of first Publication:2021/09/23
Release Date:2022/01/10
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2021
Issue:Volume 13, issue 19, article 3219
Pagenumber:21
First Page:1
Last Page:21
Tag:3D printing; fused filament fabrication; machine learning
GND Keyword:3D-Druck; Polymere; Maschinelles Lernen
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:52 Maschinenbau, Energietechnik, Fertigungstechnik / 52.72 Fertigungsautomatisierung
54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2021
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)