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Record W2040790104 · doi:10.1115/1.1331281

Bauschinger Effect Design Procedures for Compound Tubes Containing an Autofrettaged Layer

2000· article· en· W2040790104 on OpenAlexaff
Anthony P. Parker

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsAutofrettageBauschinger effectResidual stressTube (container)Materials sciencePressure vesselComposite materialCylinder stressResidualStructural engineeringStress (linguistics)EngineeringUltimate tensile strengthMathematicsPlasticity

Abstract

fetched live from OpenAlex

Autofrettage is used to introduce advantageous residual stresses into pressure vessels. The Bauschinger effect can produce less compressive residual hoop stresses near the bore than are predicted by “ideal” autofrettage solutions. A design procedure was recently proposed which models material removal from the bore or outside diameter of a single, plain autofrettaged tube in the presence of Bauschinger effect. This paper extends the procedure to model the addition of pressure or of material (via shrink-fit) to the tube, providing associated residual stress profiles following various amounts of further yielding due to a net external pressure. Simple criteria are developed for determining, and avoiding, further yielding in the autofrettaged tube when it is used as part of a compound assembly involving shrink-fitting; these criteria are based upon net pressure differential between the bore and outside diameter of the autofrettaged tube. An alternative criterion, based upon bore hoop stress, is shown to be erroneous.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.014
GPT teacher head0.251
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2000
Admission routes1
Has abstractyes

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