Fused deposition modeling of carbon‐reinforced polymer matrix composites: A comprehensive review
Bibliographic record
Abstract
Abstract Carbon‐reinforced polymer matrix composites (PMCs) have been thoroughly applied in different fields because of their benefits, such as low specific gravity, corrosion resistance, good electrical conductivity, and robust mechanical properties. Especially, with the emergence of fused deposition modeling (FDM) technology has further promoted the application of such materials in complex structural components. Recently, FDM printing carbon‐reinforced PMCs have become a hot topic in composites research, and many promising results have been achieved around related research. In order to help readers have a comprehensive and systematic understanding of the latest research progress of FDM printing carbon‐reinforced PMCs in terms of material modification, processing, material properties, and application levels, this paper reviews the properties and processes of FDM printed carbon‐reinforced PMCs and their potential applications in aerospace, flexible sensing, electrochemistry, and biomedical fields. The effects of commonly used carbon reinforcing materials on the performance of FDM printed PMCs were contrasted and analyzed. Moreover, the process optimization of printing carbon‐reinforced PMCs was introduced and highlighted. Finally, the current challenges and future research directions of FDM printing carbon‐reinforced PMCs were analyzed and prospected.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".