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Static and Dynamic Strain Aging at High Temperatures in 304 Stainless Steel

2004· article· en· W2117867647 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueISIJ International · 2004
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSofteningDynamic strain agingTorsion (gastropod)Strain rateMetallurgyAusteniteKineticsDeformation (meteorology)Compression (physics)Composite materialDiffusionStrain (injury)ThermodynamicsMicrostructure

Abstract

fetched live from OpenAlex

Commercial 304 austenitic stainless steel was deformed at high temperatures. The experiments involved 2-hit hot compression and multi-pass hot torsion testing; the experimental variables included strain rate, temperature and interpass time. The relationship between these variables and the degree of interpass softening produced unexpected results. Specifically, the normal effect of temperature on the static softening kinetics was reversed at intermediate interpass times: the fractional softening decreased with increasing temperature for these times. The diffusion kinetics and segregation mechanics of the substitutional impurities in the material, combined with the experimental results, suggest that the temporary non-equilibrium segregation of phosphorus (and/or sulphur) to dislocations is responsible for the observed behaviour. Additionally, the observed trend in strain rate sensitivity with increasing deformation temperature indicates that dynamic strain aging was taking place.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.535

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 head0.002
GPT teacher head0.207
Teacher spread0.205 · 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