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Record W3025744123 · doi:10.1149/09707.0523ecst

Electropolishing of Inside Surfaces of Stainless Steel Tubing

2020· article· en· W3025744123 on OpenAlexaff
Zahra Chaghazardi, Lucas A. Hof, Rolf Wüthrich

Bibliographic record

VenueECS Transactions · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsÉcole de Technologie SupérieureConcordia University
Fundersnot available
KeywordsElectropolishingMaterials scienceSurface roughnessSurface finishElectrodeBrightnessEccentricity (behavior)Tube (container)MetallurgyComposite materialOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

This study aims to investigate the electropolishing of inside walls of stainless steel 316 tubing using internal counter-electrodes with a focus on the effect of parameters such as interelectrode-gap, length, and the outer diameter of the tubing, and the counter-electrode eccentricity on the final roughness and brightness of the surface. It was observed that under the proper combination of electropolishing voltage and duration, irrespective of the initial surface condition of the samples, they were all significantly brightened, and their surface roughness was decreased to almost similar final values. The results indicated that the interelectrode-gap had a rather low impact on the final roughness of the surface but strongly affected its final brightness. It was also concluded that during the electropolishing process, the hydrodynamic conditions inside the tube could significantly affect the surface quality.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.220
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2020
Admission routes1
Has abstractyes

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