Rate-Controlling Microplastic Processes during Plastic Flow in FCC Metals: Origin of the Variation of Strain Rate Sensitivity in Aluminum from 78 to 300 K
Bibliographic record
Abstract
The thermodynamic response of dislocation intersections with forest dislocations and other deformation products is recorded using the Eyring rate relation wherein the application of shear stress increases the probability of activation at a given strain rate and temperature. The inverse activation volume, 1/ν, can be directly determined by instantaneous strain-rate change and its dependence on flow stress, τ, defines the strain-rate sensitivity, S, through the Haasen plot slope. A linear slope over a large strain interval is observed even for a heterogeneous distribution of obstacles that could be of more than one type of obstacles encountered by the gliding dislocation. It was deduced that ν and τ at each activation site are coordinated by the internal stress resulting in constant activation work (k/S). The stress changes from down-rate changes become larger than that from up-rate changes due to the formation of weaker obstacles, resulting in a composite S, whereas only forest dislocations are detected by the up-change. The additivity of 1/ν was used to separate obstacle species in specially prepared AA1100 and super-pure aluminum from 78 to 300 K. The deduction that repulsive intersection is the rate-controlling process and creates vacancies at each intersection site depending on temperature was validated by observing the pinning and depinning of dislocations via pipe diffusion above 125 K. A new method to separate S for dislocation-dislocation intersections from the intersections with other obstacles and their temperature dependence is presented and validated.
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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".