A Systematic Review on the Technical Performance and Sustainability of 3D Printing Filaments Using Recycled Plastic
Bibliographic record
Abstract
Over the past 40 years, global plastic production has increased twenty-fold, prompting efforts to mitigate plastic waste. Recycling has emerged as the predominant strategy for sustainable plastic waste management. As additive manufacturing (AM) continues to evolve, integrating recycled plastics with various additives has gained significant attention. This systematic literature review, conducted in full accordance with the PRISMA guidelines, aims to evaluate and compare the properties and effects of recycled plastics and their additives in AM. Specifically, it examines the thermal, mechanical, and rheological properties of these materials, along with their life cycle environmental and economic implications. A total of 88 research publications, spanning from 2013 to 2023, were analyzed. The databases searched include Scopus, Web of Science, ProQuest, and Google Scholar, with the final search conducted in December 2023. Studies were selected through a four-stage process—identification, screening, eligibility, and inclusion—based on predefined inclusion and exclusion criteria. The risk of bias was assessed using five criteria: credibility, scope, clarity, methodology, and analysis quality. The results show that most research focuses on the mechanical properties of recycled plastics, with significant gaps in understanding their thermal and rheological properties. Additionally, there is limited research on the environmental and economic viability of these materials, highlighting the need for integrated life cycle assessments and eco-efficiency analyses. This review offers additive manufacturing professionals a comprehensive understanding of the thermal, mechanical, and rheological performance of recycled plastics and additives, supporting efforts to improve sustainability in the industry.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".