Early-Stage Precipitation Phenomena and Composition-Dependent Hardening in Al-Mg-Si-(Cu) Alloys
Bibliographic record
Abstract
Al-Mg-Si-(Cu), i.e. AA6xxx, alloys are widely used light alloys which can be effectively strengthened through precipitation hardening. The final microstructure, and thus properties, of these alloys after common artificial aging treatments are largely determined by the composition-dependent nano-scale clustering and precipitation that occur during the earliest stage of aging. Therefore, multi-length scale analysis of the earliest-stage of precipitation can provide critical knowledge in understanding the basis for the microstructural evolution during aging and attaining the desired microstructures and properties. Here, we investigate the effect of alloy composition on the evolution of early-stage clusters and precipitates during aging at 180°C using high resolution transmission electron microscopy. The results map a sequential evolution of clusters with an FCC structure but different morphology/orientation characteristics. GP-zones with structures other than FCC, also form in the early stages of aging. The composition-dependent kinetics of β” phase precipitation and hardening behavior are discussed in light of the results from differential scanning calorimetry experiments, microhardness measurements, and conventional transmission electron microscopy.
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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".