Formability Characterization Using Curvature and Strain-Rate-Based Limit Strain Detection Methods Applied to Marciniak, Nakazima, and Stretch-Bend Tests
Bibliographic record
Abstract
Despite advancements in the characterization of forming limit curves (FLCs) with the development of stereoscopic digital image correlation (DIC), there is still uncertainty in the accuracy of the limit strains, especially in forming operations with out-of-plane bending. The ISO12004-2:2008 standard offers a standardized approach to FLC determination but is not without limitations and is not always applicable to new materials and forming processes (e.g., warm forming, hot stamping). In the present work, a physically based limit strain detection technique is developed, termed the Enhanced Curvature Method (ECM), based on the sheet surface curvature evolution at the onset of necking in sheet formability testing. The ECM is applied to the characterization of 1.1 mm AA5182-O sheet using Marciniak, Nakazima, and stretch–bend characterization tests, and its limit strains are compared with those from the linear best-fit (LBF) local strain-rate approach and the ISO-12004 standard. The ECM considers the physical nature of necking in sheet forming with the aid of thresholds defined in terms of an imperfection metric analogous to the well-known Marciniak–Kuczynski (MK) imperfection factor. By quantifying the evolution of necking, FLCs of different safety margins can be readily generated, enabling a more intuitive selection for the factor of safety. For lower and upper ECM thresholds, the Marciniak plane strain limiting strain was determined to lie between 0.173 and 0.198, respectively, which is comparable to the analytical prediction of 0.194 and in general agreement with the published literature for AA5182-O. Similar plane strain limits were obtained using the ISO and LBF methods with values of 0.188 and 0.208, respectively. The same rankings in limit strain values between methods were observed for plane strain loading in Nakazima and stretch–bend tests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".