Powder Recycling for the Production of Pin Fin Heat Sinks by Cold Gas Dynamic Spray
Bibliographic record
Abstract
As a result of the rise in processing power demands of today’s personal computers, water cooled pin fin heat sinks are increasingly being employed for the cooling of graphical processing units. Currently, these high performance devices are manufactured through high-cost, high-waste processes. In recent years, a new solution has emerged using the cold gas dynamic spray process, in which pin fins are directly manufactured onto a baseplate by spraying metallic powder particles through a mask. This process allows for a high degree of adaptability to different graphics processing unit shapes and sizes not achievable by any other process to date. One drawback of this process is that, as substrate sensitivity to heat and mechanical residual stresses requires the use of reduced spray parameters, there is reduced deposition efficiency, resulting in a fair portion of the feedstock powder being wasted. This work aims to demonstrate the feasibility of using powder recycling to mitigate this issue and compares coatings sprayed with reclaimed powder to their counterparts sprayed with as-received powder. The work demonstrates that cold gas dynamic spray is a highly flexible and economically competitive process for the production of pin fin heat sinks when using powder recycling even when spray parameters result in reduced deposition efficiency. The benefits of pin fins on heat transfer properties of flat plates used for graphical processing units is briefly addressed and demonstrated
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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) | 0.002 | 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".