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Record W4236251142 · doi:10.1115/pvp2003-1916

Reliability Analysis of the Tube Hydroforming Process Based on Forming Limit Diagram

2003· article· en· W4236251142 on OpenAlexaff
Bing Li, Don R. Metzger, T. J. Nye

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHydroformingForming limit diagramFormabilityReliability (semiconductor)Forming processesProcess (computing)Finite element methodLimit (mathematics)Tube (container)DiagramStructural engineeringEngineeringMechanical engineeringReliability engineeringComputer scienceMaterials scienceMathematicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Tube hydroforming has become an increasingly attractive manufacturing process in automotive industry due to it having several advantages over alternative methods. The forming limit diagram has been extensively used in metal forming as the criteria of formability. A method to assess the probability of failure of the process based on reliability theory and the forming limit diagram is proposed in this paper. The tube hydroforming process is affected by many parameters such as geometry, material properties, and process conditions. Finite element simulation was used to predict the relationship between the strain and these parameters, and a numerical method was applied to get the statistical distribution of the strain. Based on the forming limit band in the forming limit diagram, the reliability of the forming process can be evaluated. A tube hydroforming process of free bulging is then introduced as an example to illustrate the approach. The results show this reliability evaluation technique to be an innovative approach for product designers and process engineers to avoid failure during tube hydroforming.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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 teacher head, 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

Citations1
Published2003
Admission routes1
Has abstractyes

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