The development of near‐surface microstructures during hot rolling of aluminum–magnesium alloys in relation to work roll topography
Bibliographic record
Abstract
The effect of the work roll topography on the surface deformation of aluminum alloys during hot rolling was examined with the use of a rolling tribo‐simulator. AISI 52100 steel work rolls with two surface conditions, smooth (polished to a surface roughness ( R a ) of 0.01 µm) and rough (WC‐coated with a surface roughness ( R a ) of 5.68 µm), were used to hot roll Al‐Mg alloy samples under similar conditions for a rolling schedule of 10 passes. The surface of the rolled samples reflected the work roll surface morphology. Surface damage for the smooth rolled samples included cracks, while shingles and grooves were observed on the rough rolled samples. Cross‐sectional examination revealed cracks extended to depths above 8 µm for the rough rolled samples, while for the smooth rolled samples, cracks were 1.5 µm deep. The oxide‐rich near‐surface layer formed on the rough rolled surfaces was discontinuous. In contrast, the near‐surface generated by the smooth roll was continuous, and near‐surface damage was uniform in comparison. A nanocrystalline grain structure was observed at the near‐surface region beneath the oxide‐rich area for the smooth rolled samples, which extended to shingles on the rough rolled samples. The nanocrystalline nature of the near‐surface region was attributed to the high strains imposed by the work roll, while the effect of the rough roll was surmised to include the formation of shingles, the redistribution of surface oxide, and the enhancement of the near‐surface damage. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 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) | 0.001 | 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".