A CPFEM based 3D Model for Polycrystalline Plasticity with Diffused Grain Boundaries
Bibliographic record
Abstract
Understanding the relationship between the microstructure of polycrystalline materials and their macroscopic properties is critical for developing and improving them for advanced applications. In polycrystalline aggregates, the combination of computational homogenization and crystal plasticity has shown promise in simulating the effective properties and capturing such correlations. Our study uses a similar framework to model polycrystals with FCC and BCC crystal structures. The goal is to investigate the plastic deformation behavior of these materials, specifically focusing on the role of Geometrically Necessary Dislocations (GNDs). We employ a phenomenological Crystal Plasticity (CP) model on a cubic Representative Volume Element (RVE). The simulations capture the effect of crystallographic orientations, grain boundaries, and dislocation mechanisms on the deformation of polycrystalline RVE. To account for the complex behavior at grain boundaries, a diffused interface model is proposed. This model homogenizes the deformation behavior within the grain boundary region to capture more effectively the hardening due to dislocation pile-up, providing a more realistic representation of the interaction between grains. Our findings provide insights into the influence of grain boundaries on the overall strain hardening and deformation response of polycrystalline materials.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".