An Advanced Damage Percolation Model of Ductile Fracture
Bibliographic record
Abstract
A multi-scale damage percolation model has been developed to predict fracture in advanced materials with heterogeneous particle distributions. The percolation model was implemented into a commercial finite-element code using so-called “percolation elements ” to capture the complex stress- and strain-gradients that develop within the microstructure during deformation. In this approach, fracture is predicted as a direct consequence of the stress state, material properties and local conditions within the microstructure. Void nucleation, growth and coalescence models are applied for ellipsoidal voids subjected to general loading conditions. A novel void nucleation rule is employed for particle cracking based upon the particle morphology and stress state. A particle field generator has been implemented into the percolation software to generate representative particle fields based upon the field statistics obtained using x-ray micro-tomography. The percolation model was validated numerically and experimentally for an automotive-grade aluminum alloy in a notched tensile test used for material characterization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".