3D Printing and Additive Manufacturing最新文献

The use of biobased materials in additive manufacturing is a promising long-term strategy for advancing the polymer industry toward a circular economy and reducing the environmental impact. In commercial 3D printing formulations, there is still a scarcity of efficient biobased polymer resins. This research proposes vegetable oils as biobased components to formulate the stereolithography (SLA) resin. Application of nanocellulose filler, prepared from agricultural waste, remarkably improves the printed material's performance properties. The strong bonding of nanofibrillated celluloses' (NFCs') matrix helps develop a strong interface and produce a polymer nanocomposite with enhanced thermal properties and dynamical mechanical characteristics. The ultra-low NFC content of 0.1-1.0 wt% (0.07-0.71 vol%) was examined in printed samples, with the lowest concentration yielding some of the most promising results. The developed SLA resins showed good printability, and the printing accuracy was not decreased by adding NFC. At the same time, an increase in the resin viscosity with higher filler loading was observed. Resins maintained high transparency in the 500-700 nm spectral region. The glass transition temperature for the 0.71 vol% composition increased by 28°C when compared to the nonreinforced composition. The nanocomposite's stiffness has increased fivefold for the 0.71 vol% composition. The thermal stability of printed compositions was retained after cellulose incorporation, and thermal conductivity was increased by 11%. Strong interfacial interactions were observed between the cellulose and the polymer in the form of hydrogen bonding between hydroxyl and ester groups, which were confirmed by Fourier-transform infrared spectroscopy. This research demonstrates a great potential to use acrylated vegetable oils and nanocellulose fillers as a feedstock to produce high-performance resins for sustainable SLA 3D printing.

Inspired by the potential of architected materials for achieving biomimicking functionalities and the advancement of multi-material additive manufacturing to fabricate parts with complex structures and heterogeneous material distributions, this study investigates the feasibility of using a multi-material, flexible chain mail sheet for the design of an additively manufactured artificial spinal disc for reproducing patient-specific anisotropic and nonlinear rotational behaviors. The application of a chain mail-based structure is motivated by its similarities in behaviors compared with a natural disc's fiber network that likewise has negligible bending stiffness and shape-changing ability. The proposed approach for the chain mail sheet design includes an initial characterization of the uniaxial tensile responses of the chain mail unit cell defined as the basic building block of the chain mail sheet, modeling and response calculation, and material optimization. Results show that the additively manufactured chain mail sheet is not only able to exhibit a natural strain-stiffening rotational response but also is able to reproduce natural anisotropy of three natural disc specimens in the six most common rotational scenarios in daily life. This study shows the potential of additively manufactured mechanical-metamaterials-inspired structures for implant design to restore natural mechanics.

Along with the characteristic staircase effect, short carbon fibers, added to reinforce Fused Filament Fabrication parts, can significantly worsen the resulting surface finishing. Concerning this topic, the present work intends to improve the existing knowledge by analyzing 2400 measurements of arithmetic mean roughness R_a corresponding to different combinations of six process parameters: the content by weight of short carbon fibers in polyethylene terephthalate glycol (PETG) filaments f, layer height h, surface build angle θ, number of walls w, printing speed s, and extruder diameter d. The collected measurements were represented by dispersion and main effect plots. These representations indicate that the most critical parameters are θ, f, and h. Besides, up to a carbon fiber content of 12%, roughness is mainly affected by the staircase effect. Hence, it would be likely to obtain reinforced parts with similar roughness to unreinforced ones. Different machine learning methods were also tested to extract more information. The prediction model of R_a using the Random Forest algorithm showed a correlation coefficient equal to 0.94 and a mean absolute error equal to 2.026 μm. In contrast, the J48 algorithm identified a combination of parameters (h = 0.1 mm, d = 0.6 mm, and s = 30 mm/s) that, independent of the build angle, provides a R_a < 25 μm when using a 20% carbon fiber PETG filament. An example part was printed and measured to check the models. As a result, the J48 algorithm correctly classified surfaces with low roughness (R_a < 25 μm), and the Random Forest algorithm predicted the R_a value with an average relative error of less than 8%.