Neutron Diffraction Analysis of Light Alloys: A Review
Bibliographic record
Abstract
The development and application of low density alloys, such as Al and Mg alloys, has rapidly increased in the automotive sector in recent years. This necessitates advanced characterization techniques to assess the evolution of microstructure and phases during casting and processing. Further, understanding the mechanism of evolution of the defects is important in ensuring their minimization. Neutron diffraction has provided a method to determine the factors that trigger hot tearing in Al and Mg alloys as well as determining factors compromising integrity of powertrain components. In addition, neutron diffraction has been applied to examine the phase evolution during solidification of Al and Mg alloys enabling a better understanding of the effect of inoculants and solute additions on the solidification characteristics, resulting in improved castability. This paper highlights the frontiers of neutron diffraction analysis undertaken by the Centre for Near-Net-Shape Processing of Materials, Ryerson University and the CNL-Canadian Neutron Beam Centre.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".