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Record W2979673951 · doi:10.3390/met9101091

Effect of Cooling Rate on AlN Precipitation in FeCrAl Stainless Steel During Solidification

2019· article· en· W2979673951 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

VenueMetals · 2019
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMaterials sciencePrecipitationDiffusionMetallurgyVolume fractionParticle (ecology)Growth rateNitrideScanning electron microscopeThermodynamicsComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

The effect of cooling rate on the evolution of AlN inclusions precipitated during solidification in FeCrAl stainless steel was investigated using an experimental study and thermodynamic and kinetic calculations. The number and size of AlN inclusions precipitated under different cooling rates were examined with the feature function of the field-emission scanning electron microscope. A model combining micro-segregation and the diffusion-controlled growth model was set up to determine the mechanism of AlN particle growth. The results showed that AlN precipitates in the mushy zone. The size of AlN particles decreases and the number of AlN particles increases with increasing cooling rate, whereas the volume fraction is essentially unchanged. The AlN particles grow during solidification after the content of solutes in molten steel has exceeded the concentration in equilibrium with AlN. The nitrogen content varies significantly with the cooling rate during solidification. Increasing the cooling rate and reducing the nitrogen content in the molten steel can reduce the AlN particle size in FeCrAl alloys as the growth time decreases.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.229
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