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Effect of Microstructure on Fracture Mechanisms in Galvannealed Coatings.

2000· article· en· W2088048323 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

VenueISIJ International · 2000
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsGalvannealedMaterials scienceGalvanizationMetallurgyMicrostructureCoatingFracture (geology)Scanning electron microscopePhase (matter)Composite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Fracture mechanisms in galvannealed coatings have been studied by performing draw bead tests on galvannealed Ti stabilized interstitial free and Aluminum killed low carbon steel sheets and by investigating coating microstructures by scanning electron microscopy. Galvannealing treatments, on samples galvanized using an industrial hot-dip galvanizing process, were conducted at 450, 500 and 550°C for several time periods between 1 and 360 s in a laboratory induction furnace.In the coatings with low Fe content (up to 5 g/m2 ), the amount of powdering during the draw bead test was minimal. Growth of cracks nucleated within the δ1 phase was arrested at the steel-coating interfaces where only a limited amount of decohesion occurred. A steep increase in the amount of powdering was ob-servedin coatings with Fe contents between 6–9 g/m2 . In these coatings, cracks originating from the δ1 phase reached Γ–Γ1–δ1 phase boundaries, which provided preferential crack growth paths and thus facilitated fracture within the coating. A fracture mechanics model was proposed to account for the powdering resistance of galvannealed coatings.

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 categoriesInsufficient payload (model declined to judge)
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 score0.014
Threshold uncertainty score0.986

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)0.0150.000

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 head0.004
GPT teacher head0.264
Teacher spread0.259 · 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