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Record W2944666695 · doi:10.1063/1.5099817

Microstructural and magnetic Barkhausen noise characterization of temper embrittled HY-80 steel

2019· article· en· W2944666695 on OpenAlexaff
Aroba Saleem, P. R. Underhill, Nancy Herve, Shannon P. Farrell, Thomas W. Krause

Bibliographic record

VenueAIP conference proceedings · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsDefence Research and Development CanadaRoyal Military College of Canada
Fundersnot available
KeywordsMaterials scienceEmbrittlementBarkhausen effectAusteniteMetallurgyMicrostructureMagnetic fieldMagnetization

Abstract

fetched live from OpenAlex

Environmental conditions can affect the microstructural properties of a material, which ultimately determines a component’s structural integrity. The steels used in submarine applications are susceptible to embrittlement when they are heated or slowly cooled through the embrittling temperature range of 370 to 600 °C. Evaluation of the state of temper embrittlement in HY-80 steel (submarine steel) can contribute to risk assessments that provide assurance that in-service components will not undergo failure. The present work evaluated the response of Magnetic Barkhausen Noise (MBN) to changes in temper embrittlement in HY-80 cast steel. Three steel samples were subjected to a constant temperature (525 °C) at different holding times, to produce different amounts of embrittlement in each sample. The MBN measurement system used a flux controlled waveform, which facilitates reproducibility of the measurements and permits extraction of variations in permeability between samples. MBN signal response was observed to decrease as a function of holding time, which was attributed to migration of impurity elements that act as pinning sites for domain walls, to prior austenitic grain boundaries. In addition, microstructural characterization was performed on the samples using optical microscope.

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 categoriesInsufficient payload (model declined to judge)
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 score0.349
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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