Characterization of hypereutectic Al-19% Si alloy solidification process using in-situ neutron diffraction and thermal analysis techniques
Bibliographic record
Abstract
In-situ thermal analysis and neutron diffraction techniques were used simultaneously to evaluate the kinetics of the non-equilibrium solidification process of Al-19%Si binary alloy. Feasibility studies concerning the application of neutron diffraction (source: NRU nuclear reactor, Chalk River, ON) for advanced solidification analysis were undertaken to explore its potential for high resolution phase analysis. Neutron diffraction patterns were collected in the stepwise mode during solidification between 740 and 400°C. The variation of intensity of the diffraction peaks was analysed and compared to the results of a conventional cooling curve analysis. Neutron diffraction was capable of detecting nucleation of the Si phase (primary and eutectic), as well as the Al phase during Al-Si eutectic nucleation. Neutron diffraction reveals the presence of Si peaks about 23°C above the liquidus temperature, as established by thermal analysis (i.e., 672°C). This illustrates the potential of neutron diffraction for high resolution melt analysis at near-liquidus temperatures, required for advanced studies of grain refining, eutectic modification, etc. The solid-to-liquid volume fraction was determined based on the change of intensity of neutron diffraction peaks over the solidification interval. Overall, the volume determined was in good agreement with the results of the cooling curve thermal analysis. This study will help to better understand the solidification mechanism of Al-Si alloys used for various component casting applications.
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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 | 0.000 |
| 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".