Numerical Study on the Influence of Crystallographic Texture and Grain Shape on the Yield Surface of Textured Aluminum Sheet Material
Bibliographic record
Abstract
The paper investigates by numerical modeling the effects of crystallographic texture and grain shape on the shape of the yield surface of aluminum sheet material at small strains. Different representative volume elements (RVEs) of the material are considered. Plane stress state is assumed in the sheet. A rate-dependent model of crystal plasticity (CP) is used in combination with either the full-constraint (FC) Taylor model or the finite element method (FEM) to compute the volume averaged stress of the material. The effect of different crystallographic textures observed in aluminum alloys on the shape of the yield surface is firstly investigated. An analytical yield function is used to generate yield surfaces for the different crystallographic textures. The deviation between the stress states at yielding computed by FC-Taylor model and the analytical yield surface is used to evaluate the capability of the yield function to fit the anisotropic yield surfaces representing different strong crystallographic textures. Two different shapes of the grains are introduced in the RVEs of CP-FEM in order to study the effect of the grain morphology. Small effects of grain shape are found at small strain compared with the marked influence of crystallographic texture.
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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 | 0.002 |
| 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 | 0.001 | 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".