TY  - JOUR
A1  - Meiabadi, Mohammad Saleh
A1  - Moradi, Mahmoud
A1  - Karamimoghadam, Mojtaba
A1  - Ardabili, Sina
A1  - Bodaghi, Mahdi
A1  - Shokri, Manouchehr
A1  - Mosavi, Amir Hosein
T1  - Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication
T2  - polymers
N2  - 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.
KW  - 3D-Druck
KW  - Polymere
KW  - Maschinelles Lernen
KW  - 3D printing
KW  - machine learning
KW  - fused filament fabrication
KW  - OA-Publikationsfonds2021
Y1  - 2021
UR  - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/4551
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20220110-45518
UR  - https://www.mdpi.com/2073-4360/13/19/3219
VL  - 2021
IS  - Volume 13, issue 19, article 3219
SP  - 1
EP  - 21
PB  - MDPI
CY  - Basel
ER  -