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Dynamic Strain Aging during Low Cycle Fatigue Deformation in Prior Cold Worked 316L Stainless Steel

2004· article· en· W2042040976 on OpenAlexaff
Seong Gu Hong, Soon Bok Lee

Bibliographic record

VenueKey engineering materials · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMaterials scienceDynamic strain agingSofteningLow-cycle fatigueStrain rateDeformation (meteorology)Strain (injury)Composite materialPlateau (mathematics)MetallurgyStress (linguistics)Atmospheric temperature rangePlasticityAmplitudeUltimate tensile strengthThermodynamics

Abstract

fetched live from OpenAlex

Low cycle fatigue (LCF) tests were carried out in a wide temperature range (20°C-650°C)at strain rates of 1×10-4/s-1×10-2/s for 17% cold worked (CW) 316L stainless steel to investigate the conditions for the occurrence of dynamic strain aging (DSA) and its effects on material properties during LCF deformation. DSA introduced anomalous changes of LCF properties, and the DSA regime under LCF loading condition coincided with that in tensile loading condition. During LCF deformation, dynamic stain aging can be manifested in the forms of the occurrence of the plateau or the peak in the variation of cyclic peak stress with temperature, the negative temperature dependence of plastic strain amplitude or softening ratio, the negative strain rate sensitivity, and the negative strain rate dependence of plastic strain amplitude or softening ratio.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.005
GPT teacher head0.185
Teacher spread0.181 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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