Bibliographic record
Abstract
This paper describes a real time laser weld monitoring system based on detection of laser weld optical emissions in the UV, visible and IR wavelength ranges. These three signals are examined using chromatic analysis, and threshold tests are applied to various chromatic parameters to identify unacceptable weld quality. Classical chromatic analysis is the quantitative description of a color in terms of the amounts of “primary” colors that must be mixed to produce the desired color. For laser welding analysis, we extend this concept to consider UV, visible, and near IR wavelengths. The weld “color” is determined by the proportion of UV, visible, and IR light observed in the weld emission. Trichromatic coordinates can be defined as follows: X = IR/(UV + Visible + IR),Y = Visible/(UV + Visible + IR),Z = UV/(UV + Visible + IR) Any two of these values can be used to quantify the “color” of the weld. By plotting, for example, X versus Y as the weld proceeds with time, any changes in weld “color” may be observed. A stable welding process produces near constant trichromatic values. Any significant change (transient or long term) in the trichromatic coordinates indicates an anomaly in the weld.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".