Cyclic interaction between normal and shear stresses of an epoxy polymer—Experiments and model predictions
Bibliographic record
Abstract
Abstract This investigation focuses on the axial‐torsional loading interaction of an epoxy polymer, Epon 826/Epi‐Cure Curing Agent 9551. Thin‐walled tubular specimens were subjected to combined constant tensile (or shear) stress and cyclic shear (or tension) loading schemes. Pure tensile creep and shear creep tests were also performed to compare the creep deformation to that with superimposed cyclic shear or cyclic tension. Test data clearly showed that cyclic shear (or cyclic tension) have a readily discernible effect on the tensile (or shear) creep deformation. Similarly, a superimposed constant tensile (or shear) load affects the hysteresis responses in cyclic shear (or cyclic tension). A nonlinear constitutive model developed by the authors was used to simulate the observed normal‐shear stress interaction. Due to the inclusion of an effective stress parameter in its nonlinear function, this model was able to account for the normal‐shear coupling effect. However, the incorporation of a general loading/unloading rule led to inaccurate simulation of the observed oscillatory creep response. A modification of the general rule was proposed and better predictions on both the cyclic and the creep responses could be obtained. POLYM. ENG. SCI., 2010. © 2009 Society of Plastics Engineers
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".