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Record W3126525193 · doi:10.1177/1687814021989506

Influence of gap filling on mechanical properties of composite-aluminum single-lap single-bolt hybrid joints

2021· article· en· W3126525193 on OpenAlexaff
Xuande Yue, Luling An, Zengtao Chen, Chufan Wang, Yuebo Cai, Ruiheng Xiao

Bibliographic record

VenueAdvances in Mechanical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsShim (computing)Materials scienceComposite materialComposite numberLap jointStiffnessDigital image correlationUltimate tensile strengthMechanical jointStructural engineering

Abstract

fetched live from OpenAlex

Composite-aluminum structures exist in aircraft structures generally. It is easy to cause gap between mating surfaces in composite-aluminum assembled structures with the curing deformation of composite. The composite-aluminum, single-lap, single-bolt joints were utilized to investigate the influence of forced assembly, liquid shim and peelable fiberglass shim on the mechanical properties of assembled structures. A steel gasket that removed the middle part was used in the joint to make a gap. The 3D Digital Imagine Correlation (3D-DIC) system was utilized to measure the strain field of specimens and the progressive damage model was created in ABAQUS. The results show that the shim filling can significantly increase the tension stiffness and peak load of the joint compare with forced assembly. As the shim thickness changes, the effects of the liquid shim and the peelable fiberglass shim on the tensile stiffness and peak load shows different. The liquid or peelable fiberglass shim can reduce the strain value around the hole and the peelable fiberglass shim has a better result than liquid shim. The squeeze between the bolt and composite laminate has a greater impact on matrix damage and fiber-matrix shear damage, while the secondary bending has a greater impact on matrix damage and fiber damage.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.226
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 teacher head, not a consensus.

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

Citations18
Published2021
Admission routes1
Has abstractyes

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