Multifunctional hand-held laser processing device
Bibliographic record
Abstract
For the economical dismantling of nuclear installations, modular cutting systems are necessary to enable a low-emission cutting process particularly for high sheet thicknesses. Hand-held laser processing devices offer this possibility with a flexible implementation. In this paper, a newly developed hand-held laser processing device will be presented, which applies a novel bifocal optic, a special cutting system with two-gas Laval nozzles, and makes a multifunctional application area available. Laser notching, cutting, material removal, and prenotching in combination with laser cutting are already possible with this system. Extended modules for surface treatments and welding applications are under development. Besides its many fields of application, the processing device HLG 3000-sk-df offers additionally wide adaptation possibilities in a very light (3.5 kg) and compact design. This can be realized with the special mirror geometry, which combines a beam deflection mirror and a focusing lens. For the first time, the novel bifocal optic enables laser prenotching and cutting in one cycle using a hand-held laser processing device. The double focus configuration of the laser beam results from the splitted mirror design. The cutting system based on two-gas Laval nozzles constitutes a high potential for laser cutting applications resulting in high cutting qualities also for high sheet thicknesses and avoiding burr adhesion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".