The characterization of near‐surface defects evolved on aluminum–manganese alloys during hot rolling
Bibliographic record
Abstract
Hot rolling laboratory experiments were performed on an Al–Mn alloy, utilizing a rolling tribo‐simulator to characterize the evolution of near‐surface defects induced by tribological interactions between the work roll and alloy surfaces. A ten pass hot rolling schedule was conducted on Al–Mn samples using an AISI 52100 work roll polished to a surface roughness (Ra) of 0.01 μm. Micro‐cracks formed on the alloy surfaces at the first pass were observed to initiate at grain boundaries and propagate less than 1.5 μm deep into the subsurface region. The faces of the micro‐cracks in the subsurface region were observed to be covered with MgO. The surface of the samples were observed to possess a top 90‐nm‐thick oxide‐rich layer, which was mostly composed of MgO. MgAl 2 O 4 and Al 2 O 3 were also observed on the alloy surface at this stage of rolling. After ten passes, near‐surface features on the Al–Mn alloy samples included MgO‐rich islands and transverse cracks. Cracks extended more than 2 μm deep into the subsurface. Crack faces were covered with surface deformation induced crevices as well as MgO. The near‐surface was also composed of nano‐crack induced damaged areas, filled with porous nanocrystalline oxides. These damaged areas extended 0.5 μm into the subsurface region beneath a 60‐nm‐thick MgO film. The nanocrystalline oxide layers possessed embedded aluminum nano‐particles. The microstructure of the near‐surface layer suggests that magnesium diffusion to the free surfaces and crack formation play an important role in its evolution. 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".