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Record W2003102577 · doi:10.3139/146.101526

In-situ measurement of local strain partitioning in a commercial dual-phase steel

2007· article· en· W2003102577 on OpenAlexaff
Y. Ososkov, David S. Wilkinson, Mukesh Jain, Todd W. Simpson

Bibliographic record

VenueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsWestern UniversityMcMaster University
Fundersnot available
KeywordsMaterials scienceDigital image correlationMartensiteScanning electron microscopeMicrostructureDual-phase steelUltimate tensile strengthStrain (injury)Ferrite (magnet)Strain partitioningPhase (matter)In situComposite materialOptical microscopeMetallurgy

Abstract

fetched live from OpenAlex

Abstract This paper presents the results of an in-situ Scanning Electron Microscopy study of the local strain partitioning between ferrite- and martensite-rich regions in a commercial dual-phase steel. A Scanning Electron Microscopy tensile micro-stage, coupled with strain measurement methodologies based on gold micro-grids and digital image correlation, has been used to measure inhomogeneous strain fields at the micron scale. It has been found that when martensite is distributed non-uniformly, local strain partitioning depends significantly on the local spatial phase distribution and morphology. Strain distribution maps can be developed which provide valuable information about local strain paths for both phases. The results suggest that a rather detailed description of the two-phase microstructure of such materials is needed in order to fully understand their mechanical behaviour.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.065
GPT teacher head0.357
Teacher spread0.292 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations58
Published2007
Admission routes1
Has abstractyes

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