Finite volume model for laser-soot interaction for a laser transmission welding process
Bibliographic record
Abstract
Laser transmission welding, 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 semicrystalline 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 microscale model predictions for the peak temperature of the soot particle appear to be reasonable when compared with the results of the macroscale finite element models for the same process parameters and set up developed in the previous work of the authors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".