Fused Deposition Modelling of Thermoplastic Polymer Nanocomposites: A Critical Review
Bibliographic record
Abstract
Fused deposition modelling (FDM) has attracted researchers’ interest in myriads of applications. The enhancement of its part using fillers to print nanocomposites is a cutting-edge domain of research. Industrial acceptance is still a challenge, and researchers are investigating different nanofillers and polymer matrix combinations to investigate FDM-printed nanocomposites. Carbon nanotubes, graphene, and cellulose are heavily studied nanofillers because of their astonishing properties, biocompatibility, and ability to tailor the final performance of the FDM-printed nanocomposite part. This work presents a comprehensive review of polymer nanocomposites based on these nanofillers. Important examples, case studies, and results are discussed and compared to elaborate the understanding of the processing of nanocomposites, filaments, printing, and the characterisation of these nanocomposites. A detailed and exhaustive discussion of the prospective computational models, with challenges and a future road map, is provided, enabling the scientific community to understand these nanocomposites and their FDM processing for wider industrial applications and acceptance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".