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Record W4237912694 · doi:10.1515/ipp-2005-0069

Simulation of Heat Transfer in Laser Transmission Welding

2005· article· en· W4237912694 on OpenAlexaff
R. Prabhakaran, Marianna Kontopoulou, G. Zak, P. J. Bates, V. Sidiropoulos

Bibliographic record

VenueInternational Polymer Processing · 2005
Typearticle
Languageen
FieldEngineering
TopicLaser and Thermal Forming Techniques
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsMaterials scienceThermal conductionWeldingLaser beam weldingHeat transferLaserHeat-affected zoneMechanicsComputer simulationHeat transfer coefficientComposite materialOptics

Abstract

fetched live from OpenAlex

Abstract A numerical simulation of the heat transfer during laser transmission welding is presented. A finite difference approach was used to solve the one-dimensional unsteady-state heat conduction problem and to investigate the effect of welding conditions on the time-dependent temperature profiles for PA 6. For the needs of the simulation, the process was divided into heating and heat redistribution periods. The absorption coefficient of the laser-transparent part was measured experimentally and that of the laser-absorbing part was fitted using experimental data. The predicted temperature profiles were combined with experimental meltdown data to estimate the heat-affected zone thickness in the welded specimens. Good agreement was found between the estimated and measured heat-affected zone thickness values.

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)0.002
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.259
Teacher spread0.250 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

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