Estimating the Strain-Based FLC of a Tube from Straight Tube Hydroforming Experiments and Numerical Models
Bibliographic record
Abstract
The Extended Stress-Based Forming Limit Curve (XSFLC) failure criterion has been shown to provide good qualitative and quantitative predictions of failure (necking) in straight tube hydro forming when the on the level of end-feed (EF) used during hydro forming, the failure criterion has a tendency to over predict failure pressure at low Keeler-Brazier (K-B) approximation is used to define the XSFLC failure curve. Depending EF and under predict failure pressure for high EF. The over/under predictions suggest that the strain-space εFLC, which the XSFLC is based on, has too high of a plane-strain intercept (FLCo), when it is obtained using the K-B approximation (developed for sheet metal). Using the results of DP600 straight tube hydro forming experiments with EF, the hydro forming process was modeled using the finite element code LS-DYNA and a numerical parametric study was conducted to show that the FLCo, which most accurately predicted failure, was lower than the FLCo predicted by the K-B approximation for the zero and 67kN EF cases. For the 133kN EF case, the FLCo determined by the K-B approximation under predicted the measured burst pressures. This inconsistency suggests that the shape and FLCo of a tube's strain-based forming limit curve (εFLC) is different than that of sheet metal as shown by Keeler and Brazier. The coefficient of friction (COF) was also varied in the models to show the effect it has on failure prediction using the XSFLC. The COF parametric study showed that the XSFLC method is very sensitive to the COF used in the numerical models. This is most evident at high EF, when a small change in COF resulted in a large change in the predicted failure pressure.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".