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 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.

Automated summary

This study introduces a new method for creating accurate microscale finite element models of 3D printed composites. It utilizes micro-CT and neural network algorithms to differentiate and predict the distribution of fibers in the microstructure, and uses finite element analysis to determine the elastic properties of the composite material. The results show that this method is accurate and reliable, and also reveal the transversely isotropic behavior of the printed composites.