Extension of incubation models to moving surfaces irradiated by ultra-short pulse lasers
Bibliographic record
Abstract
In pulsed-laser micromachining, incubation refers to the chemical and structural change of a surface due to irradiation from a single pulse and the effect of this change on the absorption of the subsequent pulse. This ability to account for change in absorption properties of a surface – which is done through fitting of a single, material-dependent parameter – allows for prediction of the damage that will occur with irradiation and provides a pathway to uncover the complicated processes that govern incubation. However, the model as it currently stands only accounts for laser pulses irradiating a single spot on a surface. We develop, as a first step towards implementing incubation models for fabrication of surfaces with real-world applications, a mathematical description of the manner in which fluence accumulates during irradiation of surfaces moving with constant velocity relative to a beam. Within this description, we define the criteria for the accumulated fluence profile to be both fully-developed and flat and show that, when these criteria are met, the incubation models can be extended to moving surfaces. We demonstrate the necessity of such a framework with proof-of-concept experiments on three surfaces with distinct incubation behavior: glass, Titanium, and PET. Additionally, when the conditions of flat and fully-developed profile are relaxed, the cumulative and accumulated fluence profiles can differ continuously in the scanning direction. Comparison of real surfaces lased in this manner with their energy profiles can provide a new tool to further our understanding of the processes governing incubation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".