Development of a cellular automaton for a better consideration of neighborhood effect in polycrystals - Comparison with finite element method
Bibliographic record
Abstract
This paper presents the development of a cellular automaton taking into account the effects of neighborhood in the mechanical behavior of polycrystals. This model aims to have a better estimate of the elastic stress / strain field in polycrystals than conventional analytical models, such as the self-consistent model (SCM). As a first step in the consideration of neighborhood effects, the model was developed in the case of a uniaxial loading in linear elasticity. A Kelvin structure is used to represent the polycrystal in order to negate any grain size and shape effect, in order to focus primarily on the influence of crystallographic orientations. The model has been developed based on the hypotheses from a Finite Element Method (FEM) study of the influence of a grain’s neighborhood on its behavior. FEM, SCM and the analytical model here presented are finally compared grain by grain after a simulation on polycrystalline aggregates of 686 grains using the Kelvin structure. Taking the FEM results as a reference, the results of our model show an approximation almost three times better than those of the SCM which show the importance of taking into account the neighborhood effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".