Experimental study on sheet metal bending with medium-power diode laser
Bibliographic record
Abstract
In an experimental study of laser sheet bending, a 160 W diode laser is used for two-dimensional sheet bending of low-carbon steel. The variables investigated include metal sheet thickness, laser scan speed, laser power, laser beam width, and laser scan pass number. Bend surface appearances are also analysed. The laser sheet bend results demonstrate that a 940 Nm diode laser is an effective tool for laser forming of carbon steel sheets. No additional surface coating was required. The buckling mechanism may be the main source contributing to the large angle of bend found for the laser-beam-width to sheet-thickness aspect ratio close to 4; for a laser-beam-width to sheet-thickness aspect ratio of less than 2, both temperature gradient and buckling mechanisms contributed to the lower bend angles. The laser beam width study showed that, for the given material thickness range and laser beam profile, the maximum bend angle depends mainly on the material thickness, not the power intensity distribution across the bend line. However, a more evenly distributed laser beam gave the same bend angle with less material property and surface appearance changes. For obtaining the same bend angle, less laser line energy was required if a higher laser scan speed was applied, except for the extreme high-line energy level. Also, multi-path bend strategies may be preferred for maximizing the total bend angle as well as reducing bend surface morphology changes.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".