Neutron Diffraction Techniques for Alloy Characterization and Development
Bibliographic record
Abstract
Important activities in the aluminum industry are the development of new alloys, and the optimization of thermo-mechanical treatments to obtain desired performance. The strength and formability of aluminum alloys depend on the distribution and scale of precipitating phases, on the grain size and grain orientation distribution, on the distribution and scale of flaws, and on the presence of residual stresses. Thus it is useful to have detailed quantitative data on the crystal structures and volume fractions of phases that form during thermomechanical treatment, on the kinetics of solid state reactions, on the distribution of grain orientations, and on the stresses that develop during mechanical testing and forming. Neutron scattering is a powerful tool that can provide unique data to guide the development of improved materials and processes. Of particular interest are in-situ experiments: such experiments are uniquely suited to neutron diffraction because of the high penetrating power of neutrons, which allows data to be collected from materials subjected to realistic conditions (load, temperature, atmosphere) in specialized sample environments. In this presentation, we discuss several examples of neutron scattering studies, including residual strain mapping, in-situ loading experiments, texture analysis, powder diffraction, and tomography.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".