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Record W2162631245 · doi:10.1111/ffe.12253

Fatigue crack growth in 7249‐T76511 aluminium alloy under constant‐amplitude and spectrum loading

2014· article· en· W2162631245 on OpenAlexaff
James C. Newman, K.F. Walker, M. Liao

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2014
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceStructural engineeringParis' lawCrack closureAluminium alloyStress intensity factorAmplitudeTension (geology)Crack growth resistance curveCompact tension specimenFracture mechanicsComposite materialAluminiumCrackingFracture (geology)Compression (physics)EngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract Fatigue crack growth tests were conducted on compact, C(T), specimens made of 7249‐T76511 aluminium alloy. These tests were conducted to generate crack growth rate data from threshold to near fracture over a wide range of load ratios (R). Four methods were used to generate near threshold data: (1) ASTM E‐647 load reduction (LR), (2) compression pre‐cracking constant‐amplitude (CPCA), (3) compression pre‐cracking LR, and (4) constant crack mouth opening displacement LR method. A crack closure analysis was used to develop an effective stress‐intensity factor range against rate relation using a constraint factor (α = 1.85). Simulated aircraft wing spectrum tests were conducted on middle crack tension, M(T), specimens using a modified full‐scale fatigue test spectrum. The tests were used to develop the constraint‐loss regime (plane strain to plane stress; α = 1.85 to 1.15) behaviour. Comparisons were made between the spectrum tests and calculations made with the FASTRAN life prediction code; and the calculated crack growth lives were generally with ±10% of the test results.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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