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Computer Simulation of Microstructure Evolution in Low Carbon Sheet Steels

2007· article· en· W1976995808 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 · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrostructureRecrystallization (geology)Materials scienceMetallurgyPrecipitationGrain sizeGeology

Abstract

fetched live from OpenAlex

Models of microstructure evolution in steels are reviewed. The emphasis of the review is on low carbon sheet steels both hot-rolled and cold-rolled and annealed. First the state-of-the-art on industrial microstructure process models is presented. The individual model concepts for grain growth, recrystallization, precipitation and phase transformations are briefly discussed. The development from empirically-based models to physically-based models is identified as a key issue to have increased predictive capabilities for these models over a wider range of steel grades and operational conditions. The challenges in the development of the next generation of models are delineated. In particular, new aspects of microstructure evolution associated with novel processing routes and advanced high strength steels are evaluated. Further, the majority of the currently employed models are on the macro-scale but future microstructure models will increasingly be meso-scale models that predict actual microstructures rather than a number of average parameters (e.g. grain size, fraction transformed) to describe microstructure evolution.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.379

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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