Prediction of the Work-Hardening Exponent for 3104 Aluminum Sheets with Different Grain Sizes
Bibliographic record
Abstract
A practical approach to predict the yield strength and work-hardening exponent (n value) to evaluate the deep-drawing performance of annealed 3104 aluminum sheets is presented in the present work by only measuring and analyzing the grain size of the sheet. The various grain sizes were obtained through the different annealing treatment and then the evolution of the n value under different strains and the yield strength of annealed 3104 aluminum sheet were evaluated. Results showed that the n value and yield strength vary greatly with the grain size. A mathematical model relating grain size d, work-hardening exponent n, target strain ε, and yield strength Rp0.2 was developed in the present work. Within the studied grain size range d (12–29 μm), the n value generally increased with d in a strain-dependent manner, such that n = 0.1875 − 85.03 × exp [ − d / 1.94 ] when the ε was less than 0.5%, but n = 0.3 − 0.15 d − 1 / 2 when the ε was greater than 2%. On the other hand, the n value was found to depend on the target strain ε as n = 0.276 − A 1 × exp [ − e / 1.0435 ] , where A1 varies with d and its value is in the range of 0.132–0.364. In addition, the relationship between Rp0.2 and d followed the Hall-Petch equation ( R p 0.2 = 36.273 + 139.8 × d − 1 / 2 ).
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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".