The effect of layer thickness on the geometry and capillary performance of strut-based heat pipe wicks manufactured by laser powder bed fusion
Bibliographic record
Abstract
Additive manufacturing (AM) can be used to fabricate heat pipes and two-phase heat sinks which have integrated wicking structures. These devices can be customized and have superior performance when compared with devices with conventionally fabricated wicks. The current work investigates the impact of build rate on the capillary performance of AM wicks fabricated by laser powder bed fusion (LPBF). The build rate was examined by varying the layer thickness from 30 µm to 80 µm for four different strut-based wick geometries: i) body-centered cubic (BCC), ii) face-centered cubic (FCC), iii) simple cubic (SC), and iv) fluorite. The mass rate-of-rise method was used to quantify the wick hydraulic parameters (namely wick permeability, K, and effective pore radius, reff). Layer thickness has a significant influence on wick permeability; layer thickness of 40 µm result in the highest value for all configurations but have a small effect on the effective pore radius. Layer thickness of 40 µm and 60 µm reached the highest K/reff ratio, primarily because of the permeability increase. We attribute this to poor build quality such as missing struts and less defined edges which facilitate low resistance to fluid flow. The SC 60 and BCC 40 configurations achieved the maximum capillary performance with K/reff of 1.34 µm and 1.42 µm, respectively. Overall, it was found that while increasing the build rate by varying layer thickness may affect the part build quality, it promotes hydraulic performance for some configurations by creating random rough surface morphologies and arteries, which help increase permeability.
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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".