A New Constitutive Model for Work Hardening Based on Gain Boundary Energy Density
Bibliographic record
Abstract
Recently, the necessity to grade grain size to ultrafine and nano scale for understanding the mechanical behavior of these materials has been recognized. However, the nature of such classification has remained unclear. As an example, ultrafine (100 nm -1 μm) and nano (<100 nm) grained FCC metals, compared to their coarse grained counterparts, exhibit a grain size strengthening that may deviate from the Hall-Petch relationship. To explain the mechanism of such deviation, previous dislocation theories seem insufficient. To solve this problem, a critical grain size criterion governing the shift of deformation mechanism is proposed in this work. This model employs an energetic approach; it relates the grain boundary energy density to certain critical energy values; and it permits, for the first time, a quantitative grading of grain sizes. Predictions based on this model were evaluated. The prediction on copper polycrystals of various grain sizes showed a very good agreement with experimental results. It is thus wished that the grain size theory on plastic deformation mechanism could be unified with the dislocation theory. In this study, such unification is attempted by using a parameter defined as the defect energy density. The possibility of such generalization is further reasoned upon the fact that the defect energy approach should be a unique but common form applicable for both dislocations and grain boundaries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".