Hot Working Defines Thermomechanical Processing (TMP) for Aluminum Alloys and Composites
Bibliographic record
Abstract
TMP of Al alloys includes hot working with dynamic substructures and deformation bands for texture components combined with static recovery or recrystallization as well as cold working altered by annealing. The above processes are separately tailored for solute (Al, Mg), dispersoid (Al-0.7Fe) and precipitation hardening alloys; aging combined with deformation can raise strength or improve fatigue or corrosion resistance. Hot and cold rolling with suitable holding intervals are managed to combine deformation and annealing textures for planar anisotropy or for producing less fibrous grains to avoid delamination corrosion; grains may be severely refined by discontinuous or continuous recrystallization for superplastic sheet. In hot-billet and impact extrusion as an addition to substructure and texture strengthening, the intense heating near the die may be employed for precipitate solution with exit quenching for press heat treatment to T5 temper. Similarly, friction stir surface treatment and welding provide intense hot straining with additional softening as metal is swept behind the pin. In combination with some of the above, forging provides grain and dispersoid fibering oriented for crack retardation; semi solid forming competes with this.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".