Strategies for recycling multi-material polymer blends for additive manufacturing
Bibliographic record
Abstract
The rapid advancement of additive manufacturing (AM) technology, combined with the growing accumulation of plastic waste, has generated significant interest in utilizing materials derived from plastic waste and their composites within the AM industry. This paper examines the methods and approaches currently employed in recycling and blending thermoplastic waste into additive manufacturing feedstocks, aiming to enhance understanding and guide future advancements in this field. A systematic literature review including 82 papers from 2014 to 2024 was performed using the Scopus and Web of Science databases. The review findings indicate that approximately 83 % of the research is concentrated in production of new materials combining various polymer waste with recycled bio-sourced materials, recycled fillers or other additives for property enhancement. The evaluation and characterization of these new materials was carry out mostly using 3D printing, predominantly employing fused filament fabrication technology (63 %). The remaining 17 % focus on the improvement of the printing quality and optimization, development or adaptation of 3D printers for the utilization of new materials, and material reprocessability. This review highlight the need of evaluating the behavior of recycled blends over multiple life cycles, the cost and environmental assessments, and primary end-use applications of these materials, including as well as further development and design of printers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".