MétaCan
Menu
Back to cohort
Record W3036921597 · doi:10.1177/0731684420933681

Influence of freeze/thaw cycling on the mechanical performance of resistance-welded carbon fibre/polyphenylene sulphide composite joints

2020· article· en· W3036921597 on OpenAlexafffund
Vincent Rohart, Louis Laberge Lebel, Martine Dubé

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsPolytechnique MontréalÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaCentre de Recherche sur les Systèmes Polymères et Composites à Haute Performance
KeywordsMaterials scienceComposite materialCoatingComposite numberWeldingShear strength (soil)Silane

Abstract

fetched live from OpenAlex

This study evaluates the effects of freeze/thaw cycles on the mechanical performance and failure mode of resistance-welded carbon fibre/polyphenylene sulphide composite joints. Dry and moisture-saturated joints are subjected to 1000 temperature cycles varying between –40°C and 82°C. A silane coating is applied on the stainless steel mesh heating element to improve its adhesion with the polyphenylene sulphide polymer. Results show the limited impact that freeze/thaw cycles have on the lap shear strength of joints welded without any coating on the heating element. The silane coating improves the lap shear strength by 32% when no freeze/thaw cycles are applied to the joints. This improvement of 32% reduces when joints are subjected to freeze/thaw cycles but the mechanical performance remains superior to that of joints welded using uncoated heating element. Fracture surfaces show that fibre/matrix and stainless steel/matrix interfaces are both affected by the environmental conditions although it does not translate into lower lap shear strength.

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)0.000
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 score0.093
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Reinforced Plastics and CompositesSame topicMechanical Behavior of CompositesFrench-language works237,207