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Record W4407124216 · doi:10.1016/j.mtla.2025.102361

Statically recrystallized grain size as a function of prior stored energy level in the A-286 Fe-based superalloy

2025· article· en· W4407124216 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

VenueMaterialia · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsSafran Electronics (Canada)Smarter Alloys (Canada)
FundersAgence Nationale de la Recherche
KeywordsMaterials scienceSuperalloyGrain sizeMetallurgyRecrystallization (geology)Microstructure

Abstract

fetched live from OpenAlex

A-286 alloy is a Fe-based superalloy used in various engines and gas turbine components. During manufacturing, this alloy is submitted to a solution heat treatment that provides good formability for the subsequent deformation steps. Hence, a good control of grain size evolution is required to avoid the formation of a broad grain size distribution or the growth of abnormally large grains. In this work, a well-controlled strain gradient has been generated by means of indentation tests at room temperature. A specific strain level, calculated by finite element simulations, and the associated dislocation density estimated by the EBSD technique , lead to the activation of selective grain growth during heat treatment after a given incubation time. This study on cold-deformed A-286 alloy allowed a quantitative assessment of recrystallized grain size dependence on stored energy and the identification of the critical stored energy value for grain nucleation, providing a better understanding of A-286 static recrystallization behavior.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.471

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 head0.008
GPT teacher head0.212
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