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Record W2608272402 · doi:10.1520/stp159820160030

Low Cycle Fatigue of Cast Austenitic Steel

2017· book-chapter· en· W2608272402 on OpenAlexaff
Xijia Wu, Guangchun Quan, Clayton Sloss

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAusteniteMaterials scienceMetallurgyLow-cycle fatigueMicrostructure

Abstract

fetched live from OpenAlex

Cast austenitic stainless steel 1.4848 is used to manufacture automotive exhaust system components. Low cycle fatigue (LCF) of 1.4848 austenitic steel was investigated through strain controlled fatigue testing at strain rates of 0.02/s, 0.002/s, and 0.0002/s in the temperature range from room temperature (RT) to 900°C. Its cyclic behavior was characterized in relation to deformation mechanisms. At RT, the material behavior was rate independent and cyclically stable, which occurred by plasticity. The material exhibited significant cyclic hardening at intermediate temperatures, 400°C to 600°C, with negative strain-rate sensitivity. In this temperature range, dynamic strain aging (DSA) presumably occurred due to slip dragging solute atoms. At high temperatures, 800°C and 900°C, the material exhibited positive rate dependence in the hysteresis behavior, and the cyclic stress response tended to stabilize with increasing cycles. The high temperature behavior was presumably controlled by a combination of plasticity and dislocation-glide creep. The integrated creep-fatigue theory (ICFT) was used to describe the deformation and life behaviors based on the identified mechanisms, which were corroborated by fractographic observations.

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 score0.003
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.216
Teacher spread0.191 · 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
Published2017
Admission routes1
Has abstractyes

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