Decoding stress-strain parameters in FCC metals using digital constitutive analyses to devolve dynamic obstacle-strength factor and diffuse necking
Bibliographic record
Abstract
Simulation codes use constitutive relations of work hardening to virtually predict shape change during metal forming. Recent analysis has shown that the modified Hollomon-type relation correlates to stress-aided thermal activation at obstacles for dislocation movement. The sweeping action of dislocation gives rise to strain and the dislocation intersections to strain rate sensitivity during work-hardening. The constitutive relation analyses (CRA) encompass fitting parameters which remain constant with strain and its validation is the precision to replicate the measured stress-strain diagram. In this study, digital constitutive analyses (DCA) are examined whereby the modelled-fit parameters are simultaneously numerically adjusted as strain proceeds. For bulk properties such as expended work, volume fraction of vacancy creation and mean slip velocity, CRA predictions have been validated. However, DCA enables the identification of defects being created with strain using derived obstacle-strength factor (αγ). The changes in mechanisms during work-hardening can be decoded using the αγ – γ plot whereby constant αγ indicate steady-state deformation and its rapid decrease, the start of diffuse necking. Thus, αγ is a composite factor of defects being continuously created whereas conventional α is a measure of the stored work up to that strain. The tensile data from polycrystalline super-pure aluminum tested at 78 K were used to validate the derived relations which were applied to the DCA of age-hardenable aluminum alloys tested at 298 K. The present work shows that integral replication of stress-strain diagram is essential, but a differential analysis is required to devolve the creation of crystal defects with strain.
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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 | 0.000 | 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".