Neighborhood and surface effects on polycrystal stress field extreme values: An analysis in linear elastic range by means of cellular automaton
Bibliographic record
Abstract
Within polycrystals , significant stress concentrations can arise due to their heterogeneous nature. These stress intensities strongly influence the onset of nonlinear behaviors , such as plasticity and fatigue damage. One often overlooked source of heterogeneity is the crystal anisotropy and its resulting neighborhood effect. Previous research introduced a data-driven analytical model based on a cellular automaton (CA) to account for the neighborhood effect on a grain’s stress level within an infinite aggregate under elastic conditions. It was demonstrated that, in some rare specific cases, grains could experience stress levels twice as high as the applied load. The current work extends the CA model by incorporating the effects of a free surface. Randomly oriented polycrystals under uniaxial loading were studied using a regular aggregate structure (Kelvin structure), where all grains are considered spherical and of identical size. Compared to full-field simulations, the extended CA model demonstrated an excellent capability to capture heterogeneities, even in cases where high stress concentrations are generated by the neighborhood. By leveraging the model’s speed, a distribution function for grain stress levels was optimized to accurately capture the probability of extreme values. This allows for the estimation of the most likely highest stress within randomly oriented aggregates composed of billions of grains, along with its most probable localization relative to a free surface and the specific crystallographic configurations leading to it.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".