Formability Assessment of Prestrained Automotive Grade Steel Sheets Using Stress Based and Polar Effective Plastic Strain-Forming Limit Diagram
Bibliographic record
Abstract
Accurate prediction of the formability in multistage forming process is very challenging due to the dynamic shift of limiting strain during the different stages depending on the tooling geometry and selection of the process parameters. Hence, in the present work, a mathematical framework is proposed for the estimation of stress based and polar effective plastic strain-forming limit diagram (σ- and PEPS-FLD) using the Barlat-89 anisotropic plasticity theory in conjunction with three different hardening laws such as Hollomon, Swift, and modified Voce equation. Two-stage stretch forming setup had been designed and fabricated to first prestrain in an in-plane stretch forming setup, and, subsequently, limiting dome height (LDH) testing was carried out on the prestrained blanks in the second stage to evaluate the formability. The finite element (FE) analysis of these two-stage forming process was carried out in ls-dyna for automotive grade dual-phase (DP) and interstitial-free (IF) steels, and the σ-FLD and PEPS-FLD were used as damage model to predict failure. The predicted forming behaviors, such as LDH, thinning development, and the load progression, were validated with the experimental results. It was found that the LDH in the second stage decreased with increase in the prestrain amount, and both the σ-FLD and PEPS-FLD could be able to predict the formability considering the deformation histories in the present multistage forming process with complex strain path.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".