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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.…
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Gefördert aus Mitteln des Open-Access-Publikationsfonds' der Bauhaus-Universität Weimar und vom Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft (TMWWDG).
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| 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): |  Creative Commons 4.0 - Namensnennung (CC BY 4.0) |