Recycled, Bio-Based, and Blended Composite Materials for 3D Printing Filament: Pros and Cons—A Review
Bibliographic record
Abstract
In recent years, additive manufacturing (AM), known as “3D printing”, has experienced exceptional growth thanks to the development of mechatronics and materials science. Fused filament deposition (FDM) manufacturing is the most widely used technique in the field of AM, due to low operating and material costs. However, the materials commonly used for this technology are virgin thermoplastics. It is worth noting a considerable amount of waste exists due to failed print and disposable prototypes. In this regard, using green and sustainable materials is essential to limit the impact on the environment. The recycled, bio-based, and blended recycled materials are therefore a potential approach for 3D printing. In contrast, the lack of understanding of the mechanism of interlayer adhesion and the degradation of materials for FDM printing has posed a major challenge for these green materials. This paper provides an overview of the FDM technique and material requirements for 3D printing filaments. The main objective is to highlight the advantages and disadvantages of using recycled, bio-based, and blended materials based on thermoplastics for 3D printing filaments. In this work, solutions to improve the mechanical properties of 3D printing parts before, during, and after the printing process are pointed out. This paper provides an overview on choosing which materials and solutions depend on the specific application purposes. Moreover, research gaps and opportunities are mentioned in the discussion and conclusions sections of this study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".