Optimization of infill density, fiber angle, carbon fiber layer position in 3D printed continuous carbon-fiber reinforced nylon composite
Bibliographic record
Abstract
Composite materials have gained much attention in various industries, such as aerospace, automotive, sports, marine, and construction, as these sectors rely on high-performance, durable, and cost-effective materials. Such materials offer high strength, stiffness and heat resistance. However, the influence of printing parameters especially the position of carbon fiber layer on such material is rarely found in literature. The current study focuses on optimizing different printing and testing parameters such as carbon fiber layer position, infill density, fiber angle, and strain rate in 3D printed carbon-fiber reinforced nylon composite. The study also recommended the optimal combination of these parameters for maximizing the mechanical strength and energy absorption of related 3D printed parts. The investigation revealed that the most optimum condition was 80% infill density, fiber angle of 0°, carbon fiber layer position of 12–13, and strain rate of 10 mm/min. It has been found in the study that fiber angle was the most dominant input parameter with a contribution of 54.13%, whereas infill density was the second dominant parameter with a contribution of 16.25%. The study also found that the position of the carbon fiber layer has comparatively less effect on the final mechanical properties of 3D printed parts, with a contribution of 10.12%. To facilitate the optimization, the outcomes will be helpful for designing and manufacturing 3D printed carbon-fiber reinforced nylon composite parts.
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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 | 0.000 | 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 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".