Author dc.contributor.author | Meiabadi Mohammad Saleh | |
Author dc.contributor.author | Moradi Mahmoud | |
Author dc.contributor.author | Karamimoghadam Mojtaba | |
Author dc.contributor.author | Ardabili Sina | |
Author dc.contributor.author | Bodaghi Mahdi | |
Author dc.contributor.author | Shokri Manouchehr | |
Author dc.contributor.author | Mosavi Amir H. | |
Availability Date dc.date.accessioned | 2023-04-05T09:27:18Z | |
Availability Date dc.date.available | 2023-04-05T09:27:18Z | |
Release dc.date.issued | 2021 | |
Issn dc.identifier.issn | 2073-4360 | |
uri dc.identifier.uri | http://hdl.handle.net/20.500.12944/20332 | |
Abstract dc.description.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. | |
Language dc.language | en | |
Keywords dc.subject | fused filament fabrication | |
Keywords dc.subject | toughness | |
Keywords dc.subject | 3D printing | |
Keywords dc.subject | machine learning | |
Keywords dc.subject | deep learning | |
Keywords dc.subject | artificial intelligence | |
Keywords dc.subject | computational mechanics | |
Keywords dc.subject | materials design | |
Keywords dc.subject | big data | |
Keywords dc.subject | data science | |
Title dc.title | Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication | |
Type dc.type | folyóiratcikk | |
Date Change dc.date.updated | 2023-04-04T12:13:40Z | |
Version dc.description.version | kiadói | |
dc.rights.accessRights | nyílt hozzáférésű | |
dc.description.notes | „A publikáció a Nemzeti Közszolgálati Egyetem 2020. évi Tématerületi Kiválóság Program keretében, a Fenntartható biztonság és társadalmi környezet elnevezésű projekt támogatásával valósult meg, az Innovációs és Technológiai Minisztérium Nemzeti Kutatási, Fejlesztési és Innovációs Alapból nyújtott támogatásával, a Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal által kibocsátott támogatói okirat alapján.” Pályázat sorszáma: NKFIH-1273-6/2020 Támogató: NKFIH | |
Doi ID dc.identifier.doi | 10.3390/polym13193219 | |
Discipline Discipline + dc.subject.discipline | Műszaki tudományok | |
dc.subject.sciencebranch | Informatikai tudományok | |
MTMT ID dc.identifier.mtmt | 32242203 | |
dc.identifier.journalTitle | Polymers | |
dc.identifier.journalVolume | 13 | |
dc.identifier.journalIssueNumber | 19 | |
Scope dc.format.page | 1-21 | |
Wos ID dc.identifier.wos | 000709950000001 | |
ID Scopus dc.identifier.scopus | 85115790459 | |
dc.identifier.journalAbbreviatedTitle | POLYMERS-BASEL | |
Release Date dc.description.issuedate | 2021 | |
Author institution dc.contributor.department | Szoftvertervezés- és Fejlesztés Intézet | |
Author institution dc.contributor.department | Információs Társadalom Kutatóintézet | |
Author institution dc.contributor.department | Információs Társadalom Kutatóintézet | |
Author institution dc.contributor.department | Informatikai Tudományok Doktori Iskola | |
Author institution dc.contributor.department | Óbudai Egyetem | |
Author institution dc.contributor.department | Szoftvertervezés- és Fejlesztés Intézet | |
Author institution dc.contributor.department | Biztonságtudományi Doktori Iskola | |
Author institution dc.contributor.department | Felsőbbfokú Tanulmányok Intézete |