Punching with a slant angle - cutting surface quality
Bibliographic record
Abstract
For economic or process-related reasons, punching of structural sheet metal components often has to be used for car bodies. The difference in angle of attack between punch and sheet metal component is referred to as “slant angle”. However, at the current state of the art, no precise information is available on the characteristics of cutting surfaces in relation to the slant angles. For this reason, cost-intensive slider units are used for comparatively small slant angles of around 10° in order to ensure series suitability of corresponding punching processes. In this respect, recent studies performed by the authors have shown that good cutting surface qualities can also be achieved for slant angles distinctly beyond 10°. This contribution presents an empirical test series for the characterization of cutting surface parameters when punching with a slant angle. Here, the experimental cutting surface analysis showed an asymmetric characteristic of the cutting surface along the hole circumference. Furthermore, the investigated sheet metal materials HC340LA, DP600 and DP800 revealed recurring tendencies regarding the parameters “edge draw-in”, “clean cut”, “fracture surface” and “burr height”, which had been combined to corresponding three-dimensional regression models. With these regression models, cutting simulations could be calibrated, allowing a quality prognosis of cutting surfaces achievable when punching at specific slant angles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".