The Effect of 3D Representative Volume Element Grain Morphology Resolution on the Prediction of Fracture and Damage Accumulation for Multiphase Hot‐Stamped Steels
Bibliographic record
Abstract
This work investigates the effect of 3D representative volume element (RVE) grain morphology resolution on the prediction of fracture and damage evolution for tailor hot stamped steels. Scanning electron micrographs were used to build RVE meshes from coarse‐8 × 8 (<1 elements μm−3) to high‐50 × 50 (53–81 elements μm−3) element densities. For a uniaxial stress‐state, the tailored material condition (TMC1) (ferrite‐pearlite‐martensite) RVE nucleated voids due to separation between small martensite grains and the RVE fracture strain was 0.41 and 0.53 for the 50 × 50 and 25 × 25 RVE, respectively. The TMC3 (ferrite‐bainite‐martensite) RVE predicted high strain partitioning between the irregularly shaped martensite grains and the higher resolution RVE predicts a 39% lower fracture strain. The large grained martensite‐bainite TMC5 RVE predicted relatively similar void accumulation and strain partitioning behavior for the 50 × 50 and 25 × 25 RVE models, while the coarser 13 × 13 and 8 × 8 models predicted higher fracture strains due to low grain morphology resolution. Compared to an experimentally derived fracture locus, plane strain and equibiaxial deformation of the RVEs was shown to predict the equivalent fracture strains reasonably well, but like in the uniaxial case, the coarser RVEs overpredict the fracture strain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".