Article3D printing and additive manufacturing2023
Finding Ideal Parameters for Recycled Material Fused Particle Fabrication-Based 3D Printing Using an Open Source Software Implementation of Particle Swarm Optimization.
Article in 3D printing and additive manufacturing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Low-Cost Open-Source Melt Flow Index System for Distributed Recycling and Additive Manufacturing.Materials (Basel, Switzerland) · 2024Article
- Open-source cold and hot scientific sheet press for investigating polymer-based material properties.HardwareX · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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