Insight into the Influence of Punch Velocity and Thickness on Forming Limit Diagrams of AA 6061 Sheets—Numerical and Experimental Analyses
Bibliographic record
Abstract
In this article, the forming limit diagram (FLD) for aluminum 6061 sheets of thicknesses of 1 mm and 3 mm was determined numerically and experimentally, considering different punch velocities. The punch velocity was adjusted in the range of 20 mm/min to 200 mm/min during the Nakazima test. A finite element (FE) simulation was carried out by applying the Johnson–Cook material model into the ABAQUSTM FE software. In addition, a comparison between the simulation and the experimental results was made. It was observed that by increasing the punch velocity, the FLD also increased for both thicknesses, but the degree of the improvement was different. Based on these results, we found a good agreement between numerical and experimental analyses (about 10% error). Moreover, by increasing the punch velocity from 20 mm/min to 100 mm/min in 1 mm-thick specimens, the corresponding FLD increased by 3.8%, while for 3 mm-thick specimens, this increase was 5.2%; by increasing the punch velocity from 20 mm/min to 200 mm/min in the 3 mm-thick sheets, the corresponding FLD increased by 9.3%.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".