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Record W3206877283 · doi:10.1177/09544062211007172

Effect of preload and shim types on the mechanical properties of composite-aluminium bolted joints

2021· article· en· W3206877283 on OpenAlexaff
Xuande Yue, Luling An, Zengtao Chen, Yuebo Cai, Chufan Wang

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShim (computing)Materials scienceBolted jointUltimate tensile strengthComposite materialPreloadStiffnessComposite numberLap jointStructural engineeringFinite element methodEngineering

Abstract

fetched live from OpenAlex

The influence of both preload and the presence of shim types on the mechanical properties of composite-aluminium single-bolt, single-lap joints were studied in this paper. The load-displacement curve and surface strain field of joints in different shim types and preloads were obtained through tensile experiments. A progressive damage model was established using the UMAT subroutine in ABAQUS. A hybrid failure criterion and a linear continuous degradation model were used to describe the progressive damage of composite laminates. The results show that for joints with no shim and for those with various types of shims, the tensile stiffness, peak load and initial damage load could be reduced when the preload is insufficient or too large. Compared with joints with no shims or with peelable fibreglass shims, joints with liquid shims required a larger preload to achieve the best mechanical properties. As the proportion of peelable fibreglass shim increased, the tensile stiffness and peak load continued to increase in joints with a mixed shim of liquid and peelable fiberglass shim. Shims can serve as tension bearings, but have little effect on the initiation and development of bearing failure.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.064
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicMechanical Behavior of CompositesFrench-language works237,207