Decoding stress-strain diagrams to identify the evolution of crystal defects and their obstacle strengths using constitutive relations analyses
Bibliographic record
Abstract
Recent advances in microstructural characterization from the mesoscopic and microscopic scales to the nanometric and atomic scale have revealed precise details regarding the formation of nanoparticles responsible for increased strength of alloys. The size and distribution of such features have been modelled ad hoc with limited success to replicate the work-hardening behaviour. The missing issue is that of including also the evolution of the crystal defects that arise as a result of plastic flow, such as the volume fraction of nano-voids and rotated lattice structures as observed by small angle X-ray scattering (SAXS). The constitutive relation analyses (CRA) approach assumes that all shape change is due to dislocation motion, and through use of the Taylor slip model, a functional relation has been formulated to replicate the measured curve in terms of shear stress and shear strain. The CRA parameters can be correlated to the evolving deformation products by quantitatively deriving a relation for the obstacle strength factor, α, using the strain dependence of the mean slip distance, λ. Hence, the evolution of different crystal defects as straining proceeds can be assessed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".