Forensic Analyses of Stress-Strain Diagrams to Evaluate Contributions from Microstructure
Bibliographic record
Abstract
The conventional characterization of work-hardening is to approximate the stress-strain diagram using the empirical curve-fitting of Hollomon or Voce. The new method uses the Taylor slip analyses to derive a functional form which is optimally fitted to the data. This constitutive relations analysis (CRA) duplicates the data using at least two fit loci. The fit parameters relate to the slip motion within the microstructure and hence its interpretation reveals the possible dynamic shape-change reactions. The fit-process defines a new yield stress which separates the yielding from the deformation mechanisms at large strains that breaks up into two regions separated by intersection parameters. The applications of CRA to nanovoid formation and growth leading to ductile failure, plane stress yield locus prediction using tensile tests and decoding the stress-strain diagram for age-hardened aluminum alloys have been successful. Using super-pure aluminum, this study confirms that CRA is based on crystal plasticity principles and that CRA can predict the correlation of the obstacle strength factor, α, with work-hardening, hence permitting conversion of flow stress at given strains to obstacle density. The derived results show that the inherent annihilation process and the changing strength factor are coordinated to result in a self-consistent constitutive relation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".