Multiphase modelling of the growth kinetics of precipitates in Al-Cu alloys during artificial aging
Bibliographic record
Abstract
In the aluminum industry, the needs of predictability of the kinetics of precipitation during the artificial aging processes increase as the targeted applications require the maximisation of properties at the lowest costs possible. In this regard, kinetics modelling can be helpful to design the heat treatment processes. Despite using many fitting parameters, available models show a lack of fitting with experimental data, especially for the apparent heat capacity measured at high temperatures by a differential scanning calorimeter (DSC). To address this issue, a mixed-mode model was recently developed for isothermal heat treatment, whereas non-isothermal heat treatment must be considered to compare the calculated results with those measured by isochronal heating in a DSC. In this contribution, the model is extended to non-isothermal heat treatments. To this end, the growth kinetics pathway and sequence of precipitation in a binary Al-Cu alloy have been simulated, optimising the pre-exponential factor and the activation energy of the interfacial mobility of the secondary phases. This calibration of the interfacial mobilities allowed a very good reproduction of the evolution of the apparent heat capacity with temperature. The model and calibrated interfacial mobilities were then used to compute the size evolution of θ′ precipitates in an Al-4 wt%Cu. The isothermal growth rates calculated at 4 temperatures were in good agreement with those measured and reported by independent researchers. The good predictability of the model indicates that the assumptions made were suitable and well funded, especially regarding the growth rates of embryos emerging from the subcritical growth regime.
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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".