Precision micro cutting of thin steel plate with newly designed laval nozzle by pulsed YAG laser
Bibliographic record
Abstract
This paper deals with laser cutting of thin steel plate by pulsed YAG laser using newly designed Laval nozzle. The assist gas flow spouted from the Laval nozzle dose not have Mach Shock Disk in free jet, and its straightness is superior to a traditional convergent nozzle. Therefore, the pressure on workpiece increases, and the dross height can be reduced remarkably even under low supply gas pressure condition. Thus, the consumption of flow quantity of assist gas can be reduced, when the dross height is equal. Besides, color change by excessive oxidation due to the heat conduction from dross is smaller at the back side of workpiece around the kerf, since Laval nozzle can remove melted material more effectively compared to a convergent nozzle. However, the supplied gas pressure should be carefully set, since Laval nozzle has many unstable regions, where the pressure on workpiece changes periodically. The dross height dose not increase or almost equal in the case of Laval nozzle, when the gap distance between the nozzle tip and the workpiece surface is changed from 1mm to 2mm. Because the assist gas flow from Laval nozzle has an excellent straightness and the pressure on workpiece does not reduce drastically. Moreover, it was made clear that Laval nozzle designed for the special condition can reduce the dross more effectively and decrease the unstable region.
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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 | 0.000 |
| Open science | 0.001 | 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".