Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling
Bibliographic record
Abstract
A combination of thermal and mechanical processing is used to produce flat rolled aluminum products. Typically, hot rolled sheets undergo significant time at elevated temperatures during coil cooling. This results in static recrystallization. It is important to understand the linkage between the annealing schedule and the microstructure development to design robust manufacturing process that maximizes product performance and minimizes material loss in the subsequent product manufacturing. To achieve this, accurate process-microstructure linkage models are needed. This work proposes a framework capable of handling complex annealing schedules and can be used to predict microstructure evolution and the kinetics of recrystallization. The framework uses measured data like the electron backscatter diffraction maps and the annealing schedule as inputs. It uses the measured data to calculate internal variables like the stored energy and predict the evolved microstructure. The results are validated with measured data. The proposed model can further be utilized to optimize the manufacturing process while minimizing expensive plant trials.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".