Comparing the Influence of Mn and Fe Content on the Fracture of a AA6XXX Series Alloy in Different Aged States
Bibliographic record
Abstract
The objective of this study was to investigate and compare the influence of Mn and Fe additions on the fracture behaviour of AA6000 series alloys in under, peak and overaged conditions. Testing was completed under uniaxial tension and the microstructures of the alloys were observed using optical and Transmission Electron Microscopy. Alloys with very low levels of both Mn and Fe underwent a transition from transgranular to intergranular fracture and a reduction in strain-tofracture when heat treated from the under aged (UA) to peak aged (PA) condition. Increasing Mn and Fe content prevents this transition in fracture mode such that the stain-to-fracture is similar in the UA and PA states. Despite the change in fracture mode, when comparing the strain-to-fracture for a given ageing condition, increasing Fe systematically reduces the strain-to-fracture. Conversely, increases in Mn systematically increase the strain-to-fracture for a given ageing condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".