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Record W4312854237 · doi:10.31399/asm.cp.itsc2018p0179

Pin Fin Array Heat Sinks by Cold Spray Additive Manufacturing: Economics of Powder Recycling

2018· article· en· W4312854237 on OpenAlexaff
J. Perry, P. Richer, B. Jodoin, E. Matte

Bibliographic record

VenueThermal spray · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGas dynamic cold sprayMaterials scienceHeat sinkRaw materialProcess engineeringWork (physics)Process (computing)Deposition (geology)Metal powderMechanical engineeringCoatingComposite materialMetallurgyComputer scienceEngineering

Abstract

fetched live from OpenAlex

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 base plate 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 new additive manufacturing process is reduced deposition efficiency, resulting in a fair portion of the feedstock powder being wasted as substrate sensitivity to heat and mechanical residual stresses requires the use of reduced spray parameters. 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. In so doing, cold gas dynamic spray is shown to be a highly flexible and economically competitive process for the production of pin fin heat sinks even when spray parameters result in reduced deposition efficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.211
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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