Effective thermal conductivity of 3D-printed continuous fiber polymer composites
Bibliographic record
Abstract
3D printing, especially fused filament fabrication, presents a potentially attractive manufacturing technique for thermal applications such as polymer heat exchangers due to the ability to create complex internal geometries which can be used to enhance convective heat transfer. Recently, commercial and modified open-source printers have utilized continuous fibers such as carbon fiber to create continuous fiber reinforced polymer composites (FRPCs) which enhance the mechanical properties of the printed products. This continuous filler network can also serve to improve thermal conductivity. In this study, the effective thermal conductivity of 3D-printed FRPCs is characterized using a steady-state, modified, guarded hot plate apparatus. The effect of the fiber direction and volume fraction on the effective thermal conductivity of the 3D-printed composites was characterized experimentally and modeled analytically using series and parallel models. Thermal conductivities of up to 2.97 W/mK were measured for samples in which the fibers were aligned with the direction of heat flow. Measured values were in good agreement with analytical model predictions. The importance of fiber conductivity on overall performance of the FRPCs was further demonstrated using a handlaid-up, pitch-based carbon fiber sample which exhibited an effective thermal conductivity of 23.6 W/mK.
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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".