Synchronized videography of plasma plume expansion during femtosecond laser ablation
Bibliographic record
Abstract
Femtosecond lasers are gaining industrial interest for surface patterning and structuring because of the reduced heat effects to the surrounding material, resulting in a good quality product with a high aspect ratio. Analysis of the plasma plume generated during ablation can provide useful information about the laser-material interactions and thereby the quality of the resulting surface patterns. As a low-cost alternative to rather complicated ICCD camera setups, presented here is an approach based on filming the laser machining process with a high speed camera and tuning the frame rate of the camera to slightly lower than the laser pulse frequency. The delay in frequency between the laser and camera results in frames taken from sequential pulses. Each frame represents a later phase of plume expansion although taken from different pulses. Assuming equal plume evolution processes from pulse to pulse, the sequence of images obtained completes a plume expansion video. To test the assumption of homogeneity between sequential plumes, the camera can be tuned to the frequency of the laser, as to capture consecutive plumes at the same phase in their evolution. This approach enables a relatively low-cost, high resolution visualization of plasma plume evolution suitable for industrial micromachining applications with femtosecond lasers. Using this approach we illustrate differences in plume expansion at the example of machining homogeneous surface patterns in different liquid and gaseous processing environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".