A Practical Failure Limit for Sheared Edge Stretching of Automotive Body Panels
Bibliographic record
Abstract
Edge cracking is one of the major formability concerns in advanced high strength steel (AHSS) stamping. Although finite element analysis (FEA) together with the Forming Limit Diagram has been widely used, it has not effectively predicted edge cracking. Primary problems in developing a methodology to insure that parts are safe from edge cracking are the lack of an effective failure criterion and a simple and accurate measurement method that is not only usable in both die tryout and production but also can be verified by finite element analysis. The intent of this study is to develop a methodology to ensure that parts with internal cutouts, such as a body side panel can be produced without edge cracking. During tryout and production, edge cracking has traditionally been detected by visual examination, but this approach is not adequate for ensuring freedom from edge cracking. In this study, a measurement method to evaluate edge thinning has been developed and tested on a large part with dimensions similar to cutouts on body side panels. Results on edge failures for various DP600 and TRIP700 steels are presented. Edge preparation for these samples includes laser cutting and shearing with nominal clearances of 5% and 25%. The effect of directionality has also been evaluated. The test part was also used to develop a methodology for evaluating the susceptibility for edge cracking on parts during stamping process development, tryout and production. This methodology was successfully applied to a production body side panel, and the results are extended to a methodology for evaluating edge failures on production panels with interior cutouts.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".