Design of experiments to compare the reprocessing effect with Fused Deposition Modeling printing parameters on mechanical properties of Polylactic Acid specimens towards circular economy
Bibliographic record
Abstract
Distributed Recycling via Additive Manufacturing (DRAM) is a closed-loop material reprocessing solution that promotes circular economy. There are several literature gaps related to material properties and recycling cycles at different stages of the DRAM process. With an approach to filling these gaps, a small contribution has been made through this work by comparing the effect of reprocessing cycles (recycling) with the effect of FDM printing parameters such as Raster angle orientation, Infill density and Extrusion Temperature. These four parameters are ranked based on their impact on the tensile properties of Polylactic Acid (PLA) dog bone specimens. The Design of Experiments via Taguchi Analysis is carried out to avoid analysis of a large number of samples. The results show that recycling has the maximum impact on the tensile properties of PLA samples and can reduce the tensile strength by up to 75% in the course of four reprocessing cycles. The specimens had Ultimate Tensile Strength (UTS) values in the range of 20-26 MPa at the first reprocessing cycle which dropped significantly to a range of 7-9 MPa after the fourth reprocessing cycle. Additionally, a novel analysis on time and the number of specimens to be 3D printed at each reprocessing stage has also been conducted to help future researchers manage their printing schedule, especially in the recycling domain.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".