Characterization of Advanced High Strength Steel Sheets in View of the Numerical Prediction of Sidewall Curl
Bibliographic record
Abstract
In this study, a procedure for characterizing advanced high strength steel sheets is presented in view of determining the material parameters for constitutive models that can be used for accurate prediction of springback and sidewall curl. The mechanical properties of DP980 and TRIP780 sheets were obtained experimentally, and their cyclic tension-compression behaviour was modeled with the Chaboche nonlinear kinematic hardening model and the Yoshida-Uemori two-surface plasticity model that are implemented in LS-DYNA. The unloading moduli were determined from monotonic tension tests at various prestrain levels. An inverse approach based on linear and quadratic response surfaces created by Sequential Strategy with Domain Reduction (SRSM) methodology using LS-OPT software was used and investigated to identify specific material parameters in each constitutive model. It was found that the quadratic meta-model with a quadratic approximation of history variables was able to accurately reproduce the cyclic stress-strain data when multiple tension-compression cycles are considered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".