Evolution of activation energy during hot deformation of Al–15% B<sub>4</sub>C composites containing Sc and Zr
Bibliographic record
Abstract
During hot deformation, the activation energy, Q, is an essential parameter that indicates the difficulty level in the hot working processing. The evolution of the activation energies of three Al–15% B4C composites (the base material, S40 with 0.4% Sc and SZ40 with 0.4% Sc and 0.2% Zr) was investigated using high-temperature flow stress data based on a revised Sellar’s constitutive equation. The microstructure evolution during hot deformation was characterized using a transmission electron microscope. The calculated activation energy maps reveal that the activation energy during hot deformation was related to the microstructure change in addition to deformation conditions. For the base composite, the variation of the activation energy was small because the microstructure barely changed during deformation. For the Sc and Zr containing composites (S40 and SZ40), dynamic precipitation occurred at high deformation temperature and the activation energy map can be divided in two regions. The activation energy decreases with an increase of deformation temperature to the minimum level in the region I where the composites were in the solid solution condition. It follows by an increase with increasing temperature in the region II where dynamic precipitation occurred. Based on the combination of the activation energy map with the flow instability zone, the optimum hot workability of a composite in term of excellent processability was proposed at the domain where the less energy of hot deformation was required.
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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 | 0.000 |
| 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) | 0.001 | 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".