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Record W2945322305 · doi:10.2351/1.5061325

A finite volume model for laser-soot interaction for a laser transmission welding process

2008· article· en· W2945322305 on OpenAlexaff
Layla S. Mayboudi, A. M. Birk, G. Zak, Philip J. Bates

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsMaterials scienceLaserLaser beam weldingWeldingSootComposite materialFinite element methodOpticsCombustionStructural engineeringChemistry

Abstract

fetched live from OpenAlex

Laser transmission welding (LTW), a technique to join thermoplastic components, involves a laser beam passing through a laser-transmitting part being absorbed by a laser-absorbing part at the weld interface. The heat generated at the interface melts a thin layer of the plastic in both parts and forms a joint. Laser-absorbing agents such as dyes or soot particles are added to the laser-absorbing part to make it absorbing to the laser beam. Thermal and optical interaction of the soot particles and polymer with laser beam determines heating, melting, and consequently welding of plastics. To form a strong bond, it is important that the weld interface be exposed to sufficient heat to melt the polymer without degrading it. This paper investigates the thermal response of soot particles to a diode laser heat source. A thermal model is presented herein for a soot particle that is surrounded by a semi-crystalline material (PA6) and solved using finite volume technique. The results are then compared to the ones obtained from a finite element analysis solved with a commercial software (ANSYS®). The micro-scale model predictions for the peak temperature of the soot particle appear to be reasonable when compared with the results of the macro-scale finite element models for the same process parameters and set up developed in the previous work of the authors.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.040
GPT teacher head0.272
Teacher spread0.232 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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