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Record W2026568377 · doi:10.1177/0892705703016002869

Vibration Welding Nylon 66 - Part I Experimental Study

2003· article· en· W2026568377 on OpenAlexafffund
P. J. Bates, Jack MacDonald, C. Y. Wang, Jeffrey C. Mah, Hui Liang

Bibliographic record

VenueJournal of Thermoplastic Composite Materials · 2003
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsQueen's UniversityDuPont (Canada)Royal Military College of Canada
FundersAUTO21 Network of Centres of Excellence
KeywordsMaterials scienceWeldingComposite materialJoint (building)Glass fiberNylon 6Ultimate tensile strengthButt jointNylon 66VibrationComposite numberFracture (geology)FiberStructural engineeringPolymerMetallurgyPolyamide

Abstract

fetched live from OpenAlex

The strengths and failure mechanisms of vibration welded joints were studied using a central composite design of experiment. The experimental study focused on three aspects: material, joint geometry and welding process effects. Butt joints, T-joints and cup-plaque joints were made using unreinforced nylon 66 and 33% short glass fiber reinforced nylon 66. Reinforced nylon 66 exhibited lower butt and T-joint strengths than unreinforced nylon 66. Analyses of the fracture surface suggest that the lower strength for the reinforced compound is related to glass fiber bundling and orientation effects. Weld pressure appears to play the largest role in determining joint strength; lower pressure results in higher strengths. Although the nominal tensile stress on these joint geometries has been shown to be a useful screening tool, a more detailed mechanical analysis is required to understand the complex stress field existing in T- and cup-plaque joints. Such an analysis should allow a better design of real joints on industrial parts.

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)0.000
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.247
Teacher spread0.230 · 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

Citations18
Published2003
Admission routes2
Has abstractyes

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