Dislocation structure in different texture components determined by neutron diffraction line profile analysis in a highly textured Zircaloy-2 rolled plate
Bibliographic record
Abstract
A novel diffraction-based method has been developed to determine the substructure in terms of the fraction of prevailing slip systems and dislocation densities in multiple individual texture components of strongly textured materials. The method was applied to a strongly textured cold-rolled Zircaloy-2 sample, compressed along the normal direction of the rolled plate. Fourteen diffraction patterns were collected by time-of-flight neutron diffraction experiments at seven different purposely selected sample orientations. The diffraction peaks corresponding only to one single texture component out of the four prevailing components were identified and used to construct texture-specific diffraction patterns. The best five such patterns were evaluated by the convolutional multiple whole profile procedure of line profile analysis for the fractions of the prevailing 〈a〉-, 〈c + a〉- and 〈c〉-type slip systems and the corresponding dislocation densities. It is found that in the three texture components with thecaxis not parallel to the normal direction of rolling the average dislocation densities and fractions of slip-system types are the same. However, in the texture component with thecaxis parallel to the normal direction of rolling the fraction of the 〈a〉-type slip system is somewhat smaller than in the other components and that of the 〈c〉-type slip system is somewhat larger than zero. The fractions of the prevailing slip systems are in good correlation with literature data of self-consistent polycrystal plasticity modeling of Zircaloy-2.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".