Feasibility of Manufacturing and Utilizing Recycled PET 3D Printing Filaments Through Design of Experiments
Bibliographic record
Abstract
The research presented below aimed to determine the feasibility of recycling Polyethylene Terephthalate (PET) bottles through the manufacturing and use of 3D printing filaments at a Honduran University using Design of Experiments.Additive manufacturing has gained importance in recent years due to its advantages and innovative applications.It is used in various industries because it allows the creation of highly customized products and complex designs.One of its disadvantages is the cost of printing filaments; however, using filaments made from PET bottles could reduce associated costs.As a first step, a filament extruder was developed, and PET printing filament was manufactured through a filament extrusion process.The amount of filament that could be obtained from each size of PET bottle was then measured.Cylindrical specimens, 2.54 centimeters in diameter and 5.08 centimeters in height were 3D printed and subjected to compression strength tests in a hydraulic press to determine the material's strength.The strength of the PET-based specimens was compared with those made from Polylactic Acid (PLA).Finally, a factorial design with two factors, two levels, and ten replications was developed in Minitab, and the variance of the resistance of the specimens made from filaments of green and transparent bottles of two and three liters was analyzed.The results of the factorial design demonstrated a significant difference in the strength of the specimens manufactured from the four groups of bottles.This indicates that the color and volume of the bottle significantly affect the strength of the specimens printed with Polyethylene Terephthalate (PET).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".