Prediction of properties distribution of 7B50 alloy thick plates after quenching and aging by quench factor analysis method
Bibliographic record
Abstract
Abstract In the present work, continuous cooling curves were accurately measured by the modified Jominy specimen of 7B50 alloy during water‐spray quenching tests. Besides, the time–temperature–properties (TTP) curves of this alloy were obtained during isothermal treatments. Based on the accurate cooling curves and TTP curves, the hardness distribution along the thickness direction of 7B50 alloy thick plates was predicted by quench factor analysis method. It is found that the quench sensitive temperature range of 7B50 alloy is 240–410 °C, the nose temperature is 335 °C, and the incubation period at the nose temperature is about 0.87 s. When 7B50 alloy was isothermal treated at 180–400 °C after solid solution treatment (470 °C for 1 h followed by 483 °C for 2 h), the exponent ( n ) in the Johnson–Mehl–Avrami equation is close to 1 until transformed fraction of new precipitates is up to 60%, indicating that new precipitates first grow into rodlike shape and then coarsen or thicken. When the distance is less than 65 mm from the spray quenching surface of the modified Jominy specimen, the deviation between the predicted and measured hardness is less than 2.7%, confirming the quench factor analysis method as the feasible way to predict the hardness distribution along the thickness direction of 7B50 alloy thick plates. When the distance from the spray quenching surface is 25 mm, the average cooling rate in quench sensitive temperature range is 9.93 °C·s −1 , while the quench factor ( τ ) is 9.89 and the corresponding predicted hardness is HV 185.1 equivalent to 97.3% of the maximum measured hardness of 7B50 alloy in T6 temper.
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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) | 0.001 | 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) | 0.001 | 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".