Thermal Characterization of Continuous Pitch Carbon Fiber 3D-Printed using a 6-Axis Robot Arm
Bibliographic record
Abstract
Initial research into the 3D-printing of continuous pitch carbon fiber composites for heat exchanger applications has showed promise however, measured thermal conductivities were significantly lower than those predicted by theoretical models due to fiber breakage during the printing process. Prior studies in which the carbon fiber was printed at 45° from the printing bed showed through thermal testing and microscopic images that the angle of the nozzle was not enough the stop the breakage. A sample at a fiber volume percentage of 10.4% was expected to have a conductivity of 83.2 W/mK, yet experimental results showed only 36.6 W/mK. At other fiber volume percentages, a similar decrease in thermal conductivity of around 55% was found. In the present study, the effect of fiber extrusion angle on the thermal conductivity of the printed rasters is investigated using a redesigned extruder and hotend mounted to a 6-axis robot arm. The thermal conductivity of individual printed rasters was characterized using a heat flow apparatus and compared to those of un-broken and specified values. Overall, it is shown that printed fiber thermal conductivity increases from 442 W/mK at a printing angle of 45° to 621 W/mK at an angle of 60°. Future directions of this process and technology are discussed.
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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 | 0.001 |
| 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) | 0.001 | 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".