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Record W4313558083 · doi:10.1115/1.4056604

A Method of Evaluating the Driving Force and Stresses During Tube Die Expansion

2023· article· en· W4313558083 on OpenAlexaff
Zijian Zhao, Abdel‐Hakim Bouzid, Linbo Zhu

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsRotational symmetryDie (integrated circuit)Stress (linguistics)Finite element methodTube (container)MechanicsDeformation (meteorology)Materials scienceStructural engineeringMechanical engineeringComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract In this study, an analytical approach based on the energy method is used to estimate the force required to expand tubes with different die shapes. The proposed method calculates the driving force using the energy of deformation and the energy produced by friction. The new approach greatly reduces the difficulty of the analysis and simplifies the calculation. The stress distribution in the transition zone is also estimated using an analytical approach based on a self-adaption of the stress–strain curve. The approach is validated using four different numerical axisymmetric finite element models with different sizes, materials, and die shapes subjected to push and pull die expansion. Additionally, stainless steel and copper 3/8 in. tubes have been expanded with a prolate spheroid (oval) die in an experimental test bench under the two conditions of push and pull. The tangential and longitudinal strains and driving forces are monitored and recorded during the expansion process. Finally, the results from the three approaches show a very good agreement.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.297
Teacher spread0.280 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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