On the Analytical Modeling of Autofrettaged Cylinders Subjected to Bauschinger and Softening Effects
Bibliographic record
Abstract
Abstract Accurate prediction of residual stress in autofrettaged thick-walled cylinders is crucial for designing structures that can avoid failure and accurately estimate fatigue life. The strain-hardening behavior of materials during initial plastic loading and stress reversal significantly impact the evaluation of these residual stresses. Additionally, accounting for the Bauschinger effect and the reduction in elastic modulus is essential for accurately predicting stresses, particularly near the cylinder bore. An analytical model has been developed, incorporating a detailed material constitutive framework based on recent material characterization. This model considers the true strain-hardening behavior during the initial pressure loading and subsequent stress reversals, including the Bauschinger effect and potential material softening due to the reduced elastic modulus. The analysis is conducted using Hencky's deformation theory and the von Mises yield criterion. The radial, hoop, longitudinal, and equivalent stress results predicted by the analytical model are compared with those obtained from numerical simulations using the finite element method (FEM), which incorporates user-defined material models for materials such as A723-1160, HY180, and PH 13-8Mo. The close agreement between the analytical and FEM results demonstrates 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".