Confirming Computer Calculations of Phase Stability with the Experimental Observations in Automotive Alloy AA6111
Bibliographic record
Abstract
AA6111 sheet alloy has been used in automotive panel applications in North America and Europe for several years. This alloy exhibits an excellent combination of strength, formability, ageing response and surface appearance following forming and painting operations. Such a combination of properties is obtained by carefully tailoring the processing route to obtain the desired microstructure of the alloy. In recent years, the ability to predict the phase stability in different alloys has improved significantly, and it is now relatively easy to predict the particles that could form in complex multi component alloys during different processing steps. The accuracy of the predictions is dependent on whether or not the free energy expressions used in the calculations are correct. In this study, the AA6111 alloy was subjected to various annealing treatments that are reflective of different phase fields computed by the Thermo-Calc software. The particles were extracted using the phenol extraction technique and were identified using energy dispersive analysis. The interrelation of the particle analyses with the computed phase stability in AA6111 is presented.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".