Impact of ambient air medium on the surface profile of the material ablated with high-power lasers
Bibliographic record
Abstract
MIcro machining of materials with high power ultra-short-pulsed lasers is becoming a preferred technique to obtain cleaner surface characteristics. Due to the short duration of the pulse, there is insufficient time to establish the thermal equilibrium. Consequently, ablation does not pass through the melting phase. Instead, it proceeds mainly with direct removal of the material at the molecular level. To fully benefit from these properties, a high quality beam profile is required. However, during processing the optical wave front suffers distortions while passing through the medium such as air. Passage through the medium causes the beam to self-focus and the gas breaks down, thus generating plasma, which distorts the geometrical and energy profiles of the beam. This phenomenon offsets the advantages of the procedure to a certain extent. For these reasons, processing is usually conducted in vacuum with associated inconvenience and expense. As a step towards improvement over the technique, we develop a numerical scheme to determine the beam profile in air medium. The profile of the beam is then used to determine the shape of the processed surface by a geometrical method developed recently. The calculated surface profile is compared with the experimental observations with good agreement. This provides a method to develop an understanding of the interactions of the laser beam, air and the material.
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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 | 0.000 | 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 | 0.000 |
| 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".