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Record W2042021858 · doi:10.1115/1.2740307

Laser Transmission Welding of a Lap-Joint: Thermal Imaging Observations and three–dimensional Finite Element Modeling

2007· article· en· W2042021858 on OpenAlexafffund
Layla S. Mayboudi, A. M. Birk, G. Zak, P. J. Bates

Bibliographic record

VenueJournal of Heat Transfer · 2007
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceWeldingLap jointFinite element methodLaser beam weldingLaserThermalBeam (structure)OpticsJoint (building)Composite materialStructural engineeringPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Laser transmission welding (LTW) is a relatively new technology for joining plastic parts. This paper presents a three-dimensional (3D) transient thermal model of LTW solved with the finite element method. A lap-joint geometry was modeled for unreinforced polyamide (PA) 6 specimens. This thermal model addressed the heating and cooling stages in a laser welding process with a stationary laser beam. This paper compares the temperature distribution of a lap-joint geometry exposed to a stationary diode laser beam, obtained from 3D thermal modeling with thermal imaging observations. It is shown that the thermal model is capable of accurately predicting the temperature distribution when laser beam scattering during transmission through the polymer is included in the model. The weld dimensions obtained from the model have been compared with the experimental data and are in good agreement.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.025
GPT teacher head0.233
Teacher spread0.208 · 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

Citations39
Published2007
Admission routes2
Has abstractyes

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