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)-...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/13/19/3219 |
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| author | Mohammad Saleh Meiabadi Mahmoud Moradi Mojtaba Karamimoghadam Sina Ardabili Mahdi Bodaghi Manouchehr Shokri Amir H. Mosavi |
| author_facet | Mohammad Saleh Meiabadi Mahmoud Moradi Mojtaba Karamimoghadam Sina Ardabili Mahdi Bodaghi Manouchehr Shokri Amir H. Mosavi |
| author_sort | Mohammad Saleh Meiabadi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:53:50Z |
| format | Article |
| id | doaj.art-34c7e9a949ab4e16a0c6ad10f3d34282 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-10T06:53:50Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-34c7e9a949ab4e16a0c6ad10f3d342822023-11-22T16:37:26ZengMDPI AGPolymers2073-43602021-09-011319321910.3390/polym13193219Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament FabricationMohammad Saleh Meiabadi0Mahmoud Moradi1Mojtaba Karamimoghadam2Sina Ardabili3Mahdi Bodaghi4Manouchehr Shokri5Amir H. Mosavi6Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaFaculty of Engineering, Environment and Computing, School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 2JH, UKFaculty of Engineering, Environment and Computing, School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 2JH, UKDepartment of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, IranDepartment of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UKInstitute of Structural Mechanics, Bauhaus-Universität Weimar, 99423 Weimar, GermanyInstitute of Software Design and Development, Obuda University, 1034 Budapest, HungaryPolylactic 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.https://www.mdpi.com/2073-4360/13/19/3219fused filament fabricationtoughness3D printingmachine learningdeep learningartificial intelligence |
| spellingShingle | Mohammad Saleh Meiabadi Mahmoud Moradi Mojtaba Karamimoghadam Sina Ardabili Mahdi Bodaghi Manouchehr Shokri Amir H. Mosavi Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication Polymers fused filament fabrication toughness 3D printing machine learning deep learning artificial intelligence |
| title | Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication |
| title_full | Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication |
| title_fullStr | Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication |
| title_full_unstemmed | Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication |
| title_short | Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication |
| title_sort | modeling the producibility of 3d printing in polylactic acid using artificial neural networks and fused filament fabrication |
| topic | fused filament fabrication toughness 3D printing machine learning deep learning artificial intelligence |
| url | https://www.mdpi.com/2073-4360/13/19/3219 |
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