Comparative property analysis of fused filament fabrication PLA using fresh and recycled feedstocks
Bibliographic record
Abstract
Abstract Manufacturing industries generate much waste, which can be converted into raw materials for other allied industries. Thermoplastic wastes can be recycled in many cases. Recently these recycled plastics have been used to manufacture the input filament necessary for material extrusion additive manufacturing (MEAM). Polylactic acid (PLA) is one of the most common and potentially useful engineering plastics that can be additively manufactured, but the effect of recycling on the effective properties is still not well known. In this study, the mechanical properties of MEAM-processed PLA were compared for virgin and recycled feedstock. The collected data were analyzed using a full-factorial design of experiments method, providing insight into the effects of layer thickness and infill density on the performance of the material before and after recycling. The results showed a notable degradation in properties after recycling, but this effect was moderated by modifying the studied parameters. This work and its conclusions will serve as a screening study to guide future efforts in this area and promote the wider use of recycled materials in additive manufacturing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".