Accounting for local interactions in the prediction of roping of ferritic stainless steel sheets
Bibliographic record
Abstract
The effect of the spatial distribution of crystallographic orientations on roping amplitude and wavelength in ferritic stainless steel has been evaluated. The through-thickness mechanical behaviour of a sheet deformed in tension has been tested experimentally and simulated using a full-field viscoplastic fast Fourier transform formulation. These crystal plasticity simulations use orientation imaging microscopy data as input, allowing for large-scale simulation domains to be investigated while accounting for the clustering of orientations with similar deformation behaviour. The simulations predict both the local deformation response as well as the macroscopic surface roughness. The latter is compared quantitatively with experimental measurements and is shown to predict both the wavelength and amplitude of the observed roping. The results of these simulations have also been compared with previously proposed mean-field crystal plasticity simulations of roping, performed using the viscoplastic self-consistent code, in which each crystal orientation is, at most, influenced by the behaviour of a homogenized matrix, but not by its local neighbourhood. Comparison between these two kinds of approaches thus allows us to assess the significance of the local neighbourhood on the macroscopic prediction of roping.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".