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Project data
Authors Oberloier
S.
Whisman
N. G.
Joshua M. Pearce
Status Designed
Modelled
Prototyped
Verified
Verified by FAST
MOST
Links https://doi.org/10.1089/3dp.2022.0012
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Device data
Design files https://osf.io/muevs/
Hardware license CERN-OHL-S
Certifications Start OSHWA certification
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Tctscover.png Create-Joshua-Pearce.png Pearce Publications
FAST
MOST
QAS
Energy Conservation Energy Policy Industrial SymbiosisLife Cycle Analysis Materials Science Open SourceMedical Photovoltaic Systems Solar CellsSustainable Development Sustainability Education

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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.

Source

See also[edit source]

RepRapable Recyclebot and the Wild West of Recycling[edit source]


Recycling Technology[edit source]

Distributed Recycling LCA[edit source]

Literature Reviews[edit source]

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Externals[edit source]


  • Cruz, F., Lanza, S., Boudaoud, H., Hoppe, S., & Camargo, M. Polymer Recycling and Additive Manufacturing in an Open Source context: Optimization of processes and methods. [2]
  • Investigating Material Degradation through the Recycling of PLA in Additively Manufactured Parts
  • Mohammed, M.I., Das, A., Gomez-Kervin, E., Wilson, D. and Gibson, I., EcoPrinting: Investigating the use of 100% recycled Acrylonitrile Butadiene Styrene (ABS) for Additive Manufacturing.
  • Kariz, M., Sernek, M., Obućina, M. and Kuzman, M.K., 2017. Effect of wood content in FDM filament on properties of 3D printed parts. Materials Today Communications. [3]
  • Kaynak, B., Spoerk, M., Shirole, A., Ziegler, W. and Sapkota, J., 2018. Polypropylene/Cellulose Composites for Material Extrusion Additive Manufacturing. Macromolecular Materials and Engineering, p.1800037. [4]
  • O. Martikka et al., "Mechanical Properties of 3D-Printed Wood-Plastic Composites", Key Engineering Materials, Vol. 777, pp. 499-507, 2018 [5]
  • Yang, T.C., 2018. Effect of Extrusion Temperature on the Physico-Mechanical Properties of Unidirectional Wood Fiber-Reinforced Polylactic Acid Composite (WFRPC) Components Using Fused Deposition Modeling. Polymers, 10(9), p.976. [6]
  • Romani, A., Rognoli, V., & Levi, M. (2021). Design, Materials, and Extrusion-Based Additive Manufacturing in Circular Economy Contexts: From Waste to New Products. Sustainability, 13(13), 7269. https://www.mdpi.com/2071-1050/13/13/7269/pdf