Ex situ measurement of strain associated with hot tearing in AZ91D and AE42 magnesium alloys using neutron diffraction Special issue on Neutron Scattering in Canada.
Bibliographic record
Abstract
Prevention of hot tearing during casting or welding of commercial alloys remains a challenge for numerous industrial applications. The tendency of an alloy to tear is related to the alloy’s microstructure, solidification rate, and the stress/strain conditions it experiences during solidification. Due to technological challenges in performing accurate and reliable measurements, there remains a paucity of quantitative experimental data on the stress/strain conditions associated with the onset of hot hearing. This paper reports on a novel approach to quantify strain at the onset of hot tearing in two magnesium alloys. Neutron diffraction strain mapping was carried out and revealed that in the case of the AZ91D alloy, tensile strain of ∼0.05% was associated with initiation of material’s plastic damage and hot tearing, while for the AE42 alloy the critical strain was ∼0.09%.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".