MétaCan
Menu
Back to cohort
Record W4376881393 · doi:10.31399/asm.cp.itsc2023p0179

Additive Manufacturing of Channels and Surface Features Using Wire-Arc Spray

2023· article· en· W4376881393 on OpenAlexaff
Joshua Palumbo, Ramgopal Varma Ramaraju, S. Chandra

Bibliographic record

VenueThermal spray · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials sciencePolyvinyl alcoholComposite materialMoldCoatingFabricationMetalScanning electron microscopeMetallurgyThermal spraying

Abstract

fetched live from OpenAlex

Abstract Developing a cost-effective fabrication method for devices containing metal channels with surface features on the submillimeter scale is essential for the development of novel, high efficiency micro-reactors and heat sinks. Traditional methods are limited by their high cost, low geometric accuracy, high energy consumption, and long processing times. This study presents a low-cost additive manufacturing method using twin wire arc spray to make surface features at the sub-millimeter scale. Water-soluble polyvinyl alcohol (PVA) paste is first placed onto a mold containing a negative of the desired surface features and allowed to cure. The cured PVA is removed from the negative and metal sprayed onto its surface. The deposited metal film was backed by epoxy for added rigidity. The PVA paste was then dissolved in a water bath, resulting in a metal surface with the surface features of the mold. Surface features with length scales as small as 200 μm were reproduced. Coating delamination was prevented by minimizing the temperature of the substrate during spraying by increasing the standoff distance and scanning speed of the spray torch.

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 categoriesnone
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 score0.311
Threshold uncertainty score0.819

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)0.0000.000

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 head0.019
GPT teacher head0.232
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThermal spraySame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207