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Liquid phase surface nitriding of aluminium using TIG process

2014· article· en· W2042946586 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSurface Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNitridingMaterials scienceMetallurgyAluminium nitrideGas tungsten arc weldingShielding gasAluminiumIndentation hardnessNitrideScanning electron microscopeInert gasTungstenPhase (matter)Layer (electronics)Composite materialMicrostructureWeldingArc welding

Abstract

fetched live from OpenAlex

Liquid phase surface nitriding of AA5052 aluminium was performed using the heat of a TIG (tungsten inert gas) torch in a gas shielding which was a mixture of argon and nitrogen. The feasibility of obtaining nitride compounds at various TIG processing parameters and nitrogen contents in the shielding gas were studied. The presence of AlN phase was proved by X-ray diffraction analysis. Scanning electron microscopy with EDS analyser was carried out to study the morphology and chemical composition of the nitride phase. The microhardness test was also performed on cross sections of treated layers. This measurement demonstrated that the surface hardness of the untreated aluminium was increased from 52 HV for the untreated aluminium alloy to as high as 1411 HV for the nitrided sample. It was also noticed that liquid phase surface nitriding reduced the wear rate to less than quarter of that of the untreated substrate.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
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)0.0000.000

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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