Recycled, Bio-Based, and Blended Composite Materials for 3D Printing Filament: Pros and Cons—A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it