Bibliographic record
Abstract
Traditionally, the validation of three dimensional constitutive formulations (i.e. theories of plasticity) has been carried out using biaxial testing. The most widely used method for biaxial testing is the combined tension-torsion loading of thin-walled cylindrical specimens. Unfortunately, the results obtained in the past, using the incremental theory to model tension-torsion experiments involving large strains and non-proportional loading paths, are not always in agreement with observations. Two possible sources of error lie in: (i) the particular objective rate chosen for the constitutive equation, and (ii) the kinematic hardening model used to account for material anisotropy. In this study, it is demonstrated that an appropriate choice of objective stress rate can lead to improved correlation between analytical and experimental results even with the use of a simple kinematic hardening law. The evaluation is carried out using non-proportional tension-torsion loading of a thin tube. The purpose of this paper is to review the objective E-rate formulation against alternative rate formulations and demonstrate its advantage in problems involving elastic-plastic and non-proportional loading, through the finite deformation solution of tension followed by torsional loading of a thin tube. Details of the analytical thin tube solution of non-proportional tension torsion loading generalized to finite deformation plasticity is presented along with comparison of results to experiments.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".