Spatial and Temporal Characteristics of Propagating Deformation Bands in AA5182 Alloy at Room Temperature
Bibliographic record
Abstract
A luminum alloys are important technological materials primarily due to their attractive strength-to-weight ratio. They are used in diverse applications ranging from packaging to the aeronautical industry. Important candidates for such applications are the alloys from the 5000 series whose primary alloying element is Mg. They may be rolled into thin sheets and offer significant strength. However, their plastic deformation at room temperature is discontinuous, with the strain localizing in narrow bands that leave undesirable traces on the surface of the final product. This is the signature of the Portevin–Le Chatelier (PLC) phenomenon, which manifests itself in certain ranges of temperature and strain rate. The repeated strain localization is due to the negative strain rate sensitivity (SRS) of the material, which, in turn, is correlated with smaller scale phenomena associated with interactions between solute and dislocations, referred to as dynamic strain aging (DSA). The technological goal is to increase the SRS to positive values in the range of temperatures and strain rates relevant for industrial processes. This would ensure material stability during processing and would eliminate the PLC phenomenon. In particular, in Al-Mg alloys, it is desirable to increase the SRS and to eliminate the PLC effect at room temperature.
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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) | 0.003 | 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".