Effects of biaxial tensile mechanical properties and non-integer exponent on description accuracy of anisotropic yield behavior
Bibliographic record
Abstract
It is necessary to identify differences among biaxial tensile mechanical properties to describe the plastic anisotropy and potential adjustment ability of yield criteria with the non-integer exponent for the yield surface. Therefore, in this study, uniaxial and cruciform biaxial tensile tests were performed under 17 different loading paths: uniaxial tension in seven different directions, cruciform biaxial tension in rolling/transverse and 45°/135° sampling directions with seven and three different stress ratios, respectively. Based on the BBC2008 yield criterion, the uniaxial yield stresses, rθ-values, yield loci on the normal plane, and shear yield loci on the diagonal plane, predicted using six parameter identification strategies, were quantitatively evaluated for MP980, DP490, 6016-T4, and 5182-O. Results show the constraining and regulation ability of the equi-biaxial tensile data for yield loci to be better than that of near-plane strain state data. The parameter identification strategy considering the non-integer exponent was observed to significantly improved the ability of the yield criterion to describe the anisotropic yield behavior. For a simplified evaluation system that considers only the prediction accuracy of the yield locus under the principal stress state, neglecting the prediction accuracy for the shear yield locus may lead to incorrect judgments regarding the best identification strategy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".