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.