Constants for hot deformation constitutive models for recent experimental data
Bibliographic record
Abstract
This paper presents previously unavailable constants for the Sellars and Tegart constitutive model for hot metalworking. The materials considered are aluminium alloys 2024, 5083, 6061, 7050, 7075 and 356, carbon steel 1018, stainless steel 304, titanium alloy 6Al–4V, and magnesium alloys AZ31 and AZ61. These materials and their mechanical properties at high temperature are of great interest for latest generation manufacturing processes involving deformation to accomplish solid state joining, such as friction stir welding, cold spray and magnetic impulse welding. The results are also useful to model established processes, such as hot rolling, forging and creep. The methodology used to obtain the constants consists on non-linear regressions based on partial data sets as it was conducted previously. The input data were obtained from published values for hot compression experiments. All regressions presented here have a coefficient of determination R 2 >0·95. When possible, the results obtained were compared to previous published regressions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".