Scientometric analysis and critical review of fused deposition modeling in the plastic recycling context
Bibliographic record
Abstract
Plastics have emerged as one of the essential materials present on the planet. However, its accumulation can negatively impact the environment if not disposed of properly. To counter this issue, the ‘Circular Economy’ is one such economic growth model with one of the objectives of using plastic resources efficiently. Several plastic recycling methodologies have been derived, out of which Distributed Recycling via Additive Manufacturing (DRAM) is one of them. The main objective of this study aims to form an optimal link between two different areas of knowledge domains: plastic recycling and additive manufacturing. A scientometric analysis has been conducted to measure the former knowledge domains mentioned to accomplish this goal. From the results, the Scopus database yields 1452 relevant publications between 2013 and 2021. The results suggest that Fused Deposition Modeling (FDM) is the most used AM technology on recycled plastics. Hence, the review targets the FDM process in the context of plastic recycling. A critical review has been done, which shows the material characterization of recycled polymers in AM. This is followed by an in-depth analysis of the FDM technology, including discussions on influencing parameters of this process. The following results present the multi-material mixing of plastics and Direct FDM systems and their relevance in plastic recycling. These two areas create opportunities to increase the variety of feedstock materials that can be 3D printed. Lastly, the authors have proposed some future directions based on the literature review done in this work.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.046 | 0.050 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".