Weld Read-Through Defects in Laser Transmission Welding
Bibliographic record
Abstract
In Laser Transmission Welding (LTW), a laser beam passes through a transparent part and is dissipated as heat in an absorbent material through the use of laser-absorbing pigments such as carbon black (CB). This energy is then conducted further into both parts. Melting and subsequent solidification occur at the interface causing a weld to form between the two parts. Gluing or welding structures to the back of automotive Class-A panels often results in the appearance of undesirable surface deformations on the Class-A side. Through control of the laser welding and material parameters, it may be possible to use contour LTW as a means of joining structures to the back of absorbent Class-A panels without creating these unwanted surface defects. A series of lap welds was made using a range of CB levels and laser powers. A profilometer was used to measure the size and shape of the defects generated on the surface of the black part. Two types of defects were observed: ribs and sink marks. It was observed that lower powers combined with higher carbon black levels generally resulted in smaller defects. The type of defect depended on the boundary conditions between the two parts and the flow of polymer that had thermally expanded during welding (flash). If weld flash flowed into gaps between the two plates, rib defects were always observed. If flash flowed elsewhere and no gaps existed between the plates, sink marks occurred. A simple mechanism is proposed to explain these observations.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".