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Record W3084242143 · doi:10.1177/0021998320957055

Modelling resistance welding of thermoplastic composites with a nanocomposite heating element

2020· article· en· W3084242143 on OpenAlexafffund
David Brassard, Martine Dubé, Jason R. Tavares

Bibliographic record

VenueJournal of Composite Materials · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsÉcole de Technologie SupérieurePolytechnique Montréal
FundersCentre de Recherche sur les Systèmes Polymères et Composites à Haute Performance
KeywordsMaterials scienceComposite materialNanocompositeWeldingFinite element methodBrittlenessThermoplasticElectric resistance weldingFractographyUltimate tensile strengthStructural engineering

Abstract

fetched live from OpenAlex

Electrically conductive nanocomposite heating elements are being developed as a complement to traditional carbon fibre or stainless steel heating elements in resistance welding of thermoplastic composites. Here we present the development of a finite element model of the resistance welding process with these new heating elements, from which we establish a process window for high quality welded joints. The finite element model results were validated experimentally and a lap shear strength improvement of 28% is reported relative to previously published results. Fractography analysis of the broken joints revealed a thin-layer cohesive failure mode due to the brittleness of the nanocomposite heating elements.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.202
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations33
Published2020
Admission routes2
Has abstractyes

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