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Low Cycle Fatigue Behavior of 429EM Ferritic Stainless Steel at Elevated Temperatures

2004· article· en· W1967904739 on OpenAlexaff
Keum Oh Lee, Sam Son Yoon, Soon Bok Lee, Bum Shin Kim

Bibliographic record

VenueKey engineering materials · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMaterials scienceEmbrittlementHardening (computing)Ultimate tensile strengthMetallurgyStrain hardening exponentLow-cycle fatigueDynamic strain agingFatigue limitComposite material

Abstract

fetched live from OpenAlex

In recent, ferritic stainless steels are widely used in high temperature structure because of their high resistance in thermal fatigue and low prices. Tensile and low cycle fatigue(LCF) tests on 429EM stainless steel were performed at several temperatures from room temperature to 600°C. Elastic modulus, yield stress and ultimate tensile strength(UTS) decreased with increasing temperature. Considerable cyclic hardening occurred at 200°C and 400°C. 475°C embrittlement observed could not explain this phenomenon but dynamic strain aging(DSA) observed from 200°C to 500°C could explain the hardening mechanism at 200°C and 400°C. And it was observed that plastic strain energy density(PSED) was useful to predict fatigue life when large cyclic hardening occurred. Fatigue life using PSED over elastic modulus could be well predicted within 2X scatter band at various temperatures.

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)0.000
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.199
Teacher spread0.190 · 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

Citations8
Published2004
Admission routes1
Has abstractyes

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