材料科学
微尺度化学
各向同性
复合数
复合材料
有限元法
立体光刻
横观各向同性
断层摄影术
3d打印
微观结构
凯夫拉
生物医学工程
光学
结构工程
医学
物理
数学教育
数学
工程类
作者
E. Polyzos,Christina Nikolaou,D. Polyzos,Danny Van Hemelrijck,Lincy Pyl
标识
DOI:10.1016/j.addma.2023.103786
摘要
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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