A two-dimensional thermal finite element model of laser transmission welding for T joint
Bibliographic record
Abstract
Recent years have seen wider application of laser transmission welding (LTW) as a means for joining of plastic components. Advantages of LTW arise from it being a contact-free method for delivering precisely controlled energy to the surfaces of the welded components and from flexibility with regard to welding geometry afforded by the laser being under computer control. LTW involves a laser beam passing through a laser-transparent component being absorbed by the laser-absorbent component at the weld interface. Heat generated at the interface melts a thin layer of plastic in both components and thus forms a joint through molecular interdiffusion. To form a strong bond, it is important that the weld interface is exposed to sufficient heat to melt the polymer without degrading it. Delivery of the thermal energy by the laser beam is affected by process parameters, such as laser power, scan speed, beam spot size, and material properties, such as absorptivity, presence of reinforcements, and other additives. Development of a model for this thermal process capable of accurately predicting the extent of the molten zone in space and over time is vital for in-depth understanding of LTW and its optimization. This article presents a two-dimensional (2D) thermal model of LTW solved with the finite element method. A modified T-like joint geometry is modeled for unreinforced nylon 6 specimens. This thermal model addresses heating and cooling stages in a laser welding process. The 2D model is capable of predicting the molten zone depth as well as transient temperature distribution along the weld line. The molten zone predicted by the model was compared to the one observed from the cross sections of unfilled Nylon 6 modified T-joint specimens welded using a 150 W diode laser.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".