3D Printing and Additive Manufacturing最新文献

The global trend in additive manufacturing is the technology of three-dimensional (3D) printing with a high potential to avoid some of the weaknesses of conventional fabrication techniques. This new technology has been used to manufacture small tidal and wind turbines. In isolated areas, small turbines can be manufactured and assembled on-site for green energy production. The purpose of this document is to evaluate the thermomechanical behavior of a printed tidal turbine using Digimat-AM (Additive Manufacturing) with fused filament fabrication method. The finite element computes the mechanical deflection, temperature, residual stresses, and warpage fields of the printed part. The composites used during printing are thermoplastic polymers (acrylonitrile butadiene styrene, polyamide 6 [PA6], polyamide 12 [PA12], and polyetherimide [PEI]) reinforced with carbon and glass fillers in the form of fibers and beads (CF/GF and CB/GB). Through the simulation, one could show that the blade printed with PEI-CB/CF has excellent mechanical performance of low mechanical deflection and warpage, compared to PA6-CB/CF. In addition, the fiber-shaped fillers are better than the bead-shaped ones for the 3D printing process. In general, this study has shown the potential and feasibility of 3D printing as an excellent opportunity in the fabrication of small blades in the future, but more studies are required to understand this potential.

As additive manufacturing rapidly expands the number of materials including waste plastics and composites, there is an urgent need to reduce the experimental time needed to identify optimized printing parameters for novel materials. Computational intelligence (CI) in general and particle swarm optimization (PSO) algorithms in particular have been shown to accelerate finding optimal printing parameters. Unfortunately, the implementation of CI has been prohibitively complex for noncomputer scientists. To overcome these limitations, this article develops, tests, and validates PSO Experimenter, an easy-to-use open-source platform based around the PSO algorithm and applies it to optimizing recycled materials. Specifically, PSO Experimenter is used to find optimal printing parameters for a relatively unexplored potential distributed recycling and additive manufacturing (DRAM) material that is widely available: low-density polyethylene (LDPE). LDPE has been used to make filament, but in this study for the first time it was used in the open source fused particle fabrication/fused granular fabrication system. PSO Experimenter successfully identified functional printing parameters for this challenging-to-print waste plastic. The results indicate that PSO Experimenter can provide 97% reduction in research time for 3D printing parameter optimization. It is concluded that the PSO Experimenter is a user-friendly and effective free software for finding ideal parameters for the burgeoning challenge of DRAM as well as a wide range of other fields and processes.

3D printing has exhibited significant potential in outer space and medical implants. To use this technology in the specific high-value scenarios, 3D-printed parts need to satisfy quality-related requirements. In this article, the influence of the filament feeder operating states of 3D printer on the compressive properties of 3D-printed parts is studied in the fused filament fabrication process. A machine learning approach, back-propagation neural network with a genetic algorithm (GA-BPNN) optimized by k-fold cross-validation, is proposed to monitor the operating states and predict the compressive properties. Vibration and current sensors are used in situ to monitor the operating states of the filament feeder, and a set of features are extracted and selected from raw sensor data in time and frequency domains. Results show that the operating states of the filament feeder significantly affected the compressive properties of the fabricated samples, the operating states were accurately recognized with 96.3% rate, and compressive properties were successfully predicted by the GA-BPNN. This proposed method has the potential for use in industrial applications after 3D printing without requiring any further quality control.

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%.