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Record W4210684567 · doi:10.1016/j.jmrt.2022.01.109

Toward superior fatigue and corrosion fatigue crack initiation resistance of Sanicro 28 pipe super austenitic stainless steel

2022· article· en· W4210684567 on OpenAlexaff
Masoumeh Amirifard, A. Zarei‐Hanzaki, H.R. Abedi, Niloofar Eftekhari, Q. Wang

Bibliographic record

VenueJournal of Materials Research and Technology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceCorrosion fatigueCorrosionOxideAustenitic stainless steelMetallurgyStacking-fault energyAusteniteStress (linguistics)Lüders bandAlloyComposite materialMicrostructure

Abstract

fetched live from OpenAlex

The present work deals with the fatigue and corrosion-fatigue crack initiation in cold-pilgered Sanicro 28 super austenitic stainless steel. Toward this end, the stress-controlled symmetric reverse bending fatigue tests were conducted in air and mixed acid aqueous solution under the stress amplitude of 200–570 MPa with the stress ratio of −1 and frequencies of 1 and 15 Hz at ambient temperature. The fatigue study was supplemented with electrochemical experiments and advanced microstructural analyses to explore the damage characteristics in detail. The planar character of dislocation substructure owing to the low stacking fault energy of the material, and formation of parallel slip bands led to a more homogenous distribution of the imposed strain which retarded the progress of damage accumulation. According to the X-ray photoelectron spectrometer results, the formation of the insoluble and stable MoO3 oxide increased the rate of repassivation. This sticky oxide resisted against breakdown rendered by the attack of aggressive chloride ions. The corrosion fatigue behavior of the experimented alloy under the various test frequencies was discussed relying on the synergistic effects of mechanical and environmental damages.

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)0.004
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 score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.085
GPT teacher head0.348
Teacher spread0.263 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Materials Research and TechnologySame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207