High throughput surface structuring with ultrashort pulses in synchronized mode with fast polygon line scanner
Bibliographic record
Abstract
High precision laser micromachining requires an exact synchronization of the laser pulse train with the mechanical axes of the motion system to ensure for each single pulse a precise control of the laser spot position - on the target. For ultra short pulsed laser systems this was already demonstrated with a conventional two-axis galvanometer scanner. But this solution is limited by the scanner architecture to a marking speed of about 10m/s with a maximum scan line length of about 100mm. It is therefore not suited for average powers far beyond 10W when working at the optimum point with highest removal rate and machining quality is desired. A way to overcome this limitation is offered by polygon line scanners which are able to realize much higher lateral speeds at large scan line lengths. In this work we will report on the results with a polygon line scanner having a maximum moving spot velocity of 100m/s, a scan line length of 170mm, spot diameters of 45µm (1064nm) and 22µm (532nm) together with a 50W, 10-ps laser system. The precise control of the laser spot position i.e. the synchronization is realized via the new SuperSyncTM technology. Decoating, perforation and 3D patterning will act as benchmark processes to evaluate this scanning technology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".