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Record W1983147122 · doi:10.2514/1.23684

Riveting Process Induced Residual Stresses Around Solid Rivets in Mechanical Joints

2007· article· en· W1983147122 on OpenAlexaff
Calvin Rans, Paul Straznicky, René Alderliesten

Bibliographic record

VenueJournal of Aircraft · 2007
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsCarleton University
Fundersnot available
KeywordsRivetResidual stressStructural engineeringFinite element methodEngineeringStress fieldResidualMaterials scienceMechanical engineeringComposite materialComputer science

Abstract

fetched live from OpenAlex

The interference fit provided by solid rivets introduces a residual stress field beneficial to the fatigue life of riveted joints. Evolution in riveting technology has led to force-controlled riveters which provide greater consistency over the rivet installation process and the resulting residual stress field. By reexamining the rivet installation process and its effects on the formation of residual stresses, the fatigue benefits of rivets could be further exploited. Using a 3-D finite element model, installation of universal and countersunk rivets in monolithic aluminum sheet has been studied. Aspects of accepted riveting practice, including the degree of rivet flushness and the rivet squeeze force were found to play significant roles in the formation of residual stresses. Residual stresses beneath the rivet head were also found to be influenced primarily by through-thickness compression of the joined sheets during riveting, challenging the traditional analogy of riveting to radial expansion processes.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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

Citations83
Published2007
Admission routes1
Has abstractyes

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