Lattice structures
are employed as lightweight sandwich cores,
supports, or infill patterns of additive manufacturing (AM) components.
As infill structures, the mechanical properties of AM parts are influenced
by the infill pattern. In this work, we present the mechanical characterization
of three commonly used infill patterns in AM, triangular, square,
and hexagonal, and compare them with analytical and numerical models.
Fused filament fabrication of polylactic acid (PLA) thermoplastic
is used as the printing material for the compressive and tensile tests.
First, a parametric analysis is performed by changing the infill density
to obtain numerically and analytically the mechanical properties of
the studied samples. Next, we compare the experimental results with
numerical and analytical models and propose numerical correlations
for high-density honeycombs. The stiffest infill pattern was the square,
and the explanation is provided in detail. Also, there is a nonlinear
correlation between density and the mechanical properties; however,
the strongest part was not possible to determine with a significant
statistical value. Finally, we propose simplified models for predicting
the compressive and tensile response of AM PLA structures by considering
the infill regions as homogenized structures.
Continuous fiber-reinforced additive manufacturing (cFRAM) composites improve the mechanical properties of polymer components. Given the recent interest in their mechanical performance and failure mechanisms, this work aims to describe the principal failure mechanisms and compare the prediction capabilities for the mechanical properties, stiffness constants, and strength of cFRAM using two distinct predictive models. This work presents experimental tensile tests of continuous carbon fiber AM composites varying their reinforced fraction, printing direction, and fiber angle. In the first predictive model, a micromechanical-based model for stiffness and strength predicts their macroscopic response. In the second part, data-driven models using different machine learning algorithms for regression are trained to predict stiffness and strength based on critical parameters. Both models are assessed regarding their accuracy, ease of implementation, and generalization capabilities. Moreover, microstructural images are used for a qualitative evaluation of the parameters and their influence on the macroscopic response and failure surface topology. Finally, we conclude that although predicting the mechanical properties of cFRAM is a complex task, it can be carried on a Gaussian process regression and a micromechanical model, with good accuracy generalized onto different process parameters specimens.
Fatigue bending tests, under controlled displacement, were performed on a polymer matrix composite material reinforced with continuous Kevlar fibers. The samples were fabricated using the Fused Filament Fabrication (FFF) technique in a Markforged Two® 3D printer. The static characterization delivered a flexural modulus of elasticity of 4.73 GPa and flexural strength of 110 MPa. The applied loading corresponded to 92.3, 88.5, 86.2, and 84.7% of the static flexural displacement, giving 15, 248, 460, and 711 cycles for failure. Additionally, two numerical models were created: one using orthotropic properties for static loading conditions; and a second one using isotropic in-bulk properties for fatigue modeling. The second model was able to reproduce the experimental fatigue results. Finally, morphological analysis of the fractured surface revealed fiber breakage, fiber tearing, fiber buckling, matrix cracking, and matrix porosity.
Additive Manufacturing is a novel manufacturing method in which the part is produced layer by layer from a 3D CAD model. In this work, we present the mechanical characterization of Fusion Deposition Modeling (FDM). Composite parts made by a nylon matrix with two kinds of fiber reinforcements: carbon fiber or fiberglass. From the obtained microstructure, we perform a division of the composite part in regions, and individual stiffness matrices are encountered by either using a linear elastic isotropic model, for the case of solid matrix filling, or an orthotropic linear elastic model based on micromechanical results. Then, a volume average stiffness method is employed to perform the characterization of the whole part. The theoretical results are compared with the experimental data, showing good agreement for both cases. This research allows the prediction of the structural behavior of additive manufacturing 2composite parts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.