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Record W3015535596 · doi:10.1080/02670836.2020.1747187

Microstructure and texture of high manganese steel subjected to dynamic impact loading

2020· article· en· W3015535596 on OpenAlex
M. Eskandari, Jerzy A. Szpunar

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

VenueMaterials Science and Technology · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceAdiabatic shear bandSplit-Hopkinson pressure barStrain rateCrystal twinningMetallurgyMicrostructureMartensiteShear bandStrain hardening exponentSofteningComposite materialDynamic recrystallizationDeformation (meteorology)PlasticityDiffusionless transformationHot working

Abstract

fetched live from OpenAlex

Dynamic impact response of high Mn-steel at a strain rate of 3000 s −1 was investigated using the Split Hopkinson Pressure bar. The investigated steel depicted continuous yielding at high strain rates. Additionally, the yield stress displayed a positive strain-rate sensitivity with an increasing strain rate. Microstructural evaluations displayed that strain-induced martensitic transformation and dislocation multiplication during slip were dominant plastic deformation mechanisms in the absence of deformation twinning which contributes to the strain hardening. Adiabatic shear band and martensite to austenite reversion or dynamic recrystallisation were also attributed to strain softening during impact deformation. The {001}<110> R-cube, {011}<110> R-Goss, and ({111}<110>) E texture components were strengthened after impact loading compared with as-received condition, while the intensities of Cube, Cupper, Brass, and S texture components were decreased.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.261
Teacher spread0.254 · 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