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Record W2095237140 · doi:10.1520/jte20140193

Assessment of the Poisson Ratio Effect on Low Cycle Fatigue (LCF) Behavior of Shear-Cracking Mode Materials

2015· article· en· W2095237140 on OpenAlexaff
Ghassan Abed, Yung–Li Lee, John P. Quigley, Wensheng Zhang

Bibliographic record

VenueJournal of Testing and Evaluation · 2015
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsGeneral Dynamics (Canada)
Fundersnot available
KeywordsPoisson's ratioPoisson distributionMaterials scienceCrackingComposite materialStructural engineeringShear (geology)MathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract This paper evaluates the effect of different Poisson's ratios and an equivalent Poisson's ratio formula on the crack initiation angle and life estimation by using the three shear-strain based fatigue damage models (Brown–Miller, Fatemi–Socie, and Liu's Virtual Energy) on strain-controlled biaxial cyclic loading tests of specific tubular material specimens. The four shear failure mode materials studied were Inconel 718, 304L stainless steel, normalized 1050, and quenched/tempered 1050 steels. This study was motivated by the fact that for all the strain-controlled fatigue testing, the induced transverse strain was not measured, but assumed to be a negative product of the axial strain and the Poisson ratio, where the axial strain was known and given, and the Poisson ratio was either assumed to be a constant or a variable represented by a function of elastic and plastic equivalent strains. Therefore, the assumption of a Poisson's ratio in the transverse strain calculation was assessed in this study. It is concluded that the use of different Poisson's ratios in the three fatigue damage models would have some effects on the crack angle prediction, depending on the material type, and little effect on the fatigue life estimation for all materials investigated in this study.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.066
GPT teacher head0.350
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2015
Admission routes1
Has abstractyes

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