Analytical Modeling of Autofrettaged Cylinders With Consideration to Bauschinger Effect and Reduced Elastic Modulus
Bibliographic record
Abstract
Abstract The accurate prediction of residual stresses in autofrettaged thick cylinders is an absolute precondition for a successful design that avoids failure and predicts the fatigue life of these plastically deformed structures. The strain hardening material models during initial plastic loading and stress reversals play a major role in evaluating the residual stresses. In addition, the consideration of Bauschinger effect and the reduced elastic modulus are key parameters in the accurate prediction of the stresses specifically near the cylinder bore. A new analytical model that considers the accurate material constitutive model defined by the material characterization conducted by Troiano and al. [1] is developed. The true strain-hardening material behavior during the initial pressure loading and subsequent stress reversals that includes Bauschinger effect and the material possible softening due to the reduced elastic modulus are taken into consideration by the developed model. The analysis is based on the Henckey deformation theory and uses the Von Mises yield criteria. The radial, hoop, longitudinal and equivalent stress results from the analytical model are compared to the ones obtained from the numerical Finite Element Method (FEM) used in conjunction with user-defined material models that includes gun material such as A723-1160, HY180 and PH 13-8Mo. The good agreement obtained shows the robustness of the developed model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".