Finding Ideal Parameters for Recycled Material Fused Particle Fabrication-Based 3D Printing Using an Open Source Software Implementation of Particle Swarm Optimization

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.

Multi‐material distributed recycling via material extrusion: recycled high density polyethylene and poly (ethylene terephthalate) mixture

“…Subsequently, adhering to the function outlined in, Ref. 63 the fitness was calculated. A fitness value below 0.1 was considered desirable.…”

“…Establishing the optimal combinations of parameters is essential to improve the quality and mechanical properties of printed parts 62 . According to Ref., 63 particle swarm optimization (PSO) is an accurate and time‐effective method for achieving this goal. To optimize the 3D printing parameters for the rPET90//rHDPE10 material in the GigabotX, we utilized the open‐source PSO Experimenter platform, which is available for Linux.…”

“…The methodology developed by Ref. 63 was followed during the optimization. For benchmarking purposes, three artifacts were printed: a line, a plane, and a cube.…”

“…Four parameters were assessed: (1) nozzle temperature, (2) bed temperature, (3) printing speed, and (4) extrusion multiplier. 63 The initial parameters for the line are presented in Table 1a while additional parameters were obtained from preliminary experimental work, as shown in Table 1b. Finally, the PSO tuning parameters were found in the previous PSO work, 63…”

“…63 The initial parameters for the line are presented in Table 1a while additional parameters were obtained from preliminary experimental work, as shown in Table 1b. Finally, the PSO tuning parameters were found in the previous PSO work, 63…”