A study on the coupled effects of solute and grain size on the work hardening of fine- grained FCC alloys
Bibliographic record
Abstract
This thesis develops a physically based model for the work hardening of model (pure or solute strengthened) FCC alloys having grain sizes of between 2 μm and 100 μm. This model builds on a previous study of Sinclair et al. (2006) on fine-grained pure Cu and extends it to Cu16at%Ni, Cu50at%Ni and Cu1.5at%Al alloys. Through careful and systematic mechanical testing coupled with microstructural observations several basic hypotheses were tested. The yielding behaviour of fine-grained materials showed an extension of the elasto-plastic transition (over the generally accepted 0.002 offset strain) with decreasing grain size and increasing solute content. This resulted in Hall-Petch plots which showed that the grain size effect was more pronounced with increasing solute content. In all of the materials tested with a sufficiently fine grain size, the stress-strain plots showed an inflection (i.e. region of a low work hardening or “plateau”). While the work hardening rate dropped significantly in this portion of the test, image correlation was used to show that the drop in work hardening was not sufficient to cause strain localization. The stress-strain plots were differentiated and work hardening behaviour was analyzed using a Kocks-Mecking model. An important observation was that with increasing solute content from pure Cu to Cu50at%Ni, a grain size dependent separation between the work hardening plots appeared for tests performed at room temperature. This observation was initially hypothesized to be due to backstresses as proposed in the original model of Sinclair (Sinclair et al. 2006). This idea was tested using strain-rate sensitivity experiments. Strain-rate sensitivity tests showed that a single mechanism (forest hardening) controls the work hardening behaviour beyond the initial few percentage of straining. To unify all these experimental observations in a self-consistent work hardening model, the original Sinclair model was modified through the addition of a new variable, n*f , which accounts for additional dislocation storage by the forest dislocations blocked at grain boundaries. It was hypothesized that the effects of dislocation/grain boundary interactions on screening/annihilation of dislocations could be used to capture the initial high rate sensitivity at the “plateau” in the stress-strain curve of fine grained alloys.
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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".