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Record W3175198777 · doi:10.31399/asm.cp.itsc2021p0235

A Novel Modeling Method to Study the Oxide Layer Effect on Metallic Bonding in Cold Gas Dynamic Spray Process

2021· article· en· W3175198777 on OpenAlexaff
S. Rahmati, B. Jodoin, Roberto Gomes de Aguiar Veiga, Alejandro Zúñiga

Bibliographic record

VenueThermal spray · 2021
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceOxideLayer (electronics)Explosive materialProcess (computing)WeldingExplosion weldingGas dynamic cold sprayFinite element methodSubstrate (aquarium)Particle (ecology)MetalMetallic bondingMetallurgyComposite materialMechanical engineeringStructural engineeringComputer scienceEngineeringCoatingChemistryFiller metal

Abstract

fetched live from OpenAlex

Abstract In this study, a new physically-based finite element approach is proposed to model and predict the superficial oxide layer removal and the occurrence of localized metallic bonding during particle impacts. The process physics, based on explosive welding theory and experiments, and method implementation is presented. Prediction of critical velocity of copper is obtained and compared to experimental data to validate the model. Moreover, the model is also able to show the bonding locations at the interface between particles and substrate. The predicted bonding locations are consistent with experimental data from literature for several metals.

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)0.001
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 score0.244
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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