Direct modeling of the elastic properties of single 3D printed composite filaments using X-ray computed tomography images segmented by neural networks
This study introduces a new method for creating accurate microscale finite element (FE) models of 3D printed composites. The approach involves utilizing conventional micro-computed tomography (micro-CT) and neural network algorithms and is applied to single 3D printed composite filaments that are re...
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| Vydané v: | Additive manufacturing Ročník 76; s. 103786 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
25.08.2023
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| ISSN: | 2214-8604, 2214-7810 |
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| Abstract | This study introduces a new method for creating accurate microscale finite element (FE) models of 3D printed composites. The approach involves utilizing conventional micro-computed tomography (micro-CT) and neural network algorithms and is applied to single 3D printed composite filaments that are reinforced with Kevlar fibers. Initially, images from micro-CT scans are processed using the YOLOv7 (you only look once) algorithm to differentiate the fibers in the micro-CT images, resulting in an accurate representation of the fibers in the microstructure. The fibers are then integrated into representative volume elements (RVEs) that are simulated using the FE method to predict the effective elastic properties of the 3D printed composite. The results are compared with experiments and indicate that this approach leads to accurate predictions of the elastic properties. Additionally, it is demonstrated that the printed filaments display transversely isotropic behavior, with the axis of isotropy aligned with the length of the printed filament. These findings highlight the potential of this approach for ameliorating the design and production of 3D printed composites. |
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| AbstractList | This study introduces a new method for creating accurate microscale finite element (FE) models of 3D printed composites. The approach involves utilizing conventional micro-computed tomography (micro-CT) and neural network algorithms and is applied to single 3D printed composite filaments that are reinforced with Kevlar fibers. Initially, images from micro-CT scans are processed using the YOLOv7 (you only look once) algorithm to differentiate the fibers in the micro-CT images, resulting in an accurate representation of the fibers in the microstructure. The fibers are then integrated into representative volume elements (RVEs) that are simulated using the FE method to predict the effective elastic properties of the 3D printed composite. The results are compared with experiments and indicate that this approach leads to accurate predictions of the elastic properties. Additionally, it is demonstrated that the printed filaments display transversely isotropic behavior, with the axis of isotropy aligned with the length of the printed filament. These findings highlight the potential of this approach for ameliorating the design and production of 3D printed composites. |
| ArticleNumber | 103786 |
| Author | Polyzos, D. Van Hemelrijck, D. Pyl, L. Polyzos, E. Nikolaou, C. |
| Author_xml | – sequence: 1 givenname: E. orcidid: 0000-0002-1787-3081 surname: Polyzos fullname: Polyzos, E. email: Efstratios.Polyzos@vub.be organization: Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium – sequence: 2 givenname: C. surname: Nikolaou fullname: Nikolaou, C. organization: Department of Mechanical Engineering and Aeronautics, University of Patras, GR26500 Patras, Greece – sequence: 3 givenname: D. surname: Polyzos fullname: Polyzos, D. organization: Department of Mechanical Engineering and Aeronautics, University of Patras, GR26500 Patras, Greece – sequence: 4 givenname: D. orcidid: 0000-0002-5228-9307 surname: Van Hemelrijck fullname: Van Hemelrijck, D. organization: Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium – sequence: 5 givenname: L. orcidid: 0000-0001-7509-1718 surname: Pyl fullname: Pyl, L. organization: Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium |
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| Keywords | Homogenization 3D printed composites Neural network algorithms Micro-computed tomography YOLOv7 |
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