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Record W2277759077 · doi:10.1063/1.4942824

Effects of high-strain-rate deformation on magnetic hysteresis in high-tensile steels

2016· article· en· W2277759077 on OpenAlexaff
Ryo MORITA, Satoru Kobayashi, A.G. Odeshi, Jerzy A. Szpunar, Kodai Miura, Yasuhiro Kamada

Bibliographic record

VenueAIP Advances · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceStrain rateMicrostructureCoercivityUltimate tensile strengthMagnetic hysteresisHysteresisDeformation (meteorology)Composite materialAnisotropyStrain (injury)MetallurgyCondensed matter physicsMagnetizationMagnetic fieldOptics

Abstract

fetched live from OpenAlex

We have studied a relationship between magnetic hysteresis and microstructures on high-tensile AISI 4340 steels after impact loading with a strain rate up to 2100 s−1 We find that coercivity, and minor-loop coefficient which is deduced from a power-law scaling between minor-loop parameters increase with strain rate, show a maximum at around a strain rate of 1400 s−1, followed by a decrease at a higher strain rate, associated with magnetic anisotropy with respect to impact direction. These observations are explained from the viewpoints of heat generation and heterogeneous microstructures characteristic to steels subjected to high strain rate deformation.

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 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 score0.107
Threshold uncertainty score0.489

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 communication0.0000.001
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 head0.241
Teacher spread0.233 · 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 teacher head, 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

Citations2
Published2016
Admission routes1
Has abstractyes

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