Analytical Formulation to Predict Residual Stresses in Thick-Walled Cylinders Subjected to Hoop Winding, Shrink-Fit, and Conventional and Reverse Autofrettages
Bibliographic record
Abstract
Abstract Shrink-fit, wire-winding, and autofrettage processes and their combinations can be effectively used to increase the strength and fatigue life of metallic thick-walled cylinders for a given volume. While several numerical solutions have been developed for determining the residual stress profile through the thickness of thick-walled cylinders for different combinations of the shrink-fit and autofrettage processes, there are no analytical solutions available to predict the residual stress profile induced by the combination of the shrink-fit and inner and outer autofrettage processes with the hoop winding. In this study, the analytical formulations to predict the residual stress distribution for various combinations of the three processes (hoop-winding, shrink-fit, and autofrettage) have been formulated considering the same manufacturing sequences. The results demonstrate that combinations that include the wire-winding process significantly improve the residual stress profile through the wall thickness of single- or two-layer thick-walled cylinders. Specifically, when the wire-winding process is included, the residual stress at the inner surface increases by 25% in single-layer configurations and by 12% in two-layer thick-walled cylinders, respectively, compared to configurations without the wire-winding process.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".