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Record W2617733941 · doi:10.1177/0021998317710708

Effect of Z-pinning on fatigue crack propagation in composite skin/stiffener structures

2017· article· en· W2617733941 on OpenAlexaff
Xiangyang Zhang, Suong V. Hoa, Yong Li, Jun Xiao, Yan Tan

Bibliographic record

VenueJournal of Composite Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsConcordia University
FundersAeronautical Science Foundation of ChinaPriority Academic Program Development of Jiangsu Higher Education InstitutionsChina Scholarship Council
KeywordsMaterials scienceComposite numberUltimate tensile strengthComposite materialStructural engineeringCrack closureMonotonic functionFracture mechanicsMathematicsEngineering

Abstract

fetched live from OpenAlex

The skin/stiffener interface debonding has been a longstanding problem for composite stiffened panels. Proper crack-arresting reinforcements become a necessity for the wide application on large-scale framed structures. Z-pinning was employed to strengthen composite skin/stiffener bond in this study. Herein, static and fatigue tensile tests were conducted on a generic configuration to characterize the improvement of Z-pinning on skin/stiffener debonding resistance to skin stretching. Results show that the improvements on ultimate debonding strength and fatigue life are significant, even though the effect on crack onset is marginal under either monotonic or cyclic loading. Z-pinning changes the unstable continuous crack growth into a propagation-suspension-propagation evolution pattern. The crack growth rate is decreased by up to three orders of magnitude due to Z-pinning. Effects of pin distribution were experimentally studied. A locally densified distribution is found to be more effective than the traditional uniform distribution.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.053
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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