A numerical method to predict the rate-sensitive hardening behaviour of sheet materials using uniaxial and biaxial flow curves
Bibliographic record
Abstract
The use of advanced high strength steel (AHSS) is increasing in the automotive industry due to their remarkable strength-to-weight ratio and formability. In recent years, there has been a keen interest to employ high-energy rate forming processes such as electromagnetic and electrohydraulic forming because they can significantly improve the formability of these materials. However, simulating these forming processes requires reliable hardening functions that can accurately predict their flow behaviour in a wide range of strains and strain rates. One of the limitations of uniaxial tension tests is that the maximum uniform strain is not sufficient to calibrate a hardening function at high strain levels. In this work, a new numerical method is proposed to generate the extended flow curves of DP600 and TRIP780 from uniaxial tension data obtained at strain rates ranging from 0.001s−1 to 1000s−1 and from balanced biaxial tension data obtained under quasi-static conditions. Then, a 7-parameter strain-rate dependent Voce hardening function, which accounts for stage IV hardening, was fitted to the true stress-strain curves thus generated. Finally, statistical analysis was used to evaluate the goodness of the fit of predicted results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".