Failure Prediction in Hydroforming of Pre-Bent HSLA Tube Using an Extended Stress-Based Forming Limit Curve
Bibliographic record
Abstract
This paper examines the prediction of failure during the hydroforming of pre-bent HSLA350 tubes using the Extended Stress-Based Forming Limit Curve (XSFLC) [1]. The process of obtaining a strain-based forming limit curve (ε-FLC) for the tube and its application to the prediction of failure in tube hydroforming, utilizing the XSFLC, is presented in detail. The XSFLC was obtained from ε-FLC that was calibrated using the results of free expansion tube burst tests. Tube bending and hydroforming experiments were carried out and modeled using the dynamic explicit finite element code, LS-DYNA. An LS-DYNA user subroutine that utilizes the XSFLC to predict the onset of necking was used to model the tube material. The predicted failure location and pressure at the onset of necking were found to be in a good agreement with the experimental results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".