Complementarity of impact shot displacements and induced residual stress fields for a reliable estimation of crystal viscoplasticity parameters
Bibliographic record
Abstract
This work investigates the possibility to identify two crystal plasticity viscoplastic parameters K and n using two different outputs produced by the high velocity impact of a sphere onto a metallic sample: the shot displacement and the different components of the induced residual stress field on a cross-section under the dent. The identifiability of the two parameters using either the shot displacement, the residual stress field or the combination of both outputs is investigated using the sensitivity of each field to a variation of the coefficients as well as an identifiability indicator, I, representative of the problem well posedness. This work demonstrates that identification of K and n using only the displacement curve is an ill-posed problem, even when combining the displacements obtained with different impact conditions. The residual stress field under the dent is proved to be rich enough to obtain the two coefficients using any of the in-plane stress components. Combining two stress components for identification results only in a slightly better conditioning of the problem. Finally, combining the shot displacement curve with a single component of the residual stress field obtained for the same test greatly improves the value of I. This result demonstrates that those two outputs provide complementary information for identification of the two coefficients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".