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Record W2079390885 · doi:10.1115/esda2012-82931

Modelling of Tubes Magnetic Pulse Welding

2012· article· en· W2079390885 on OpenAlexaff
A. Guglielmetti, Nicolas Buiron, Daniel Marceau, Mohamed Rachik, C. Volat

Bibliographic record

VenueVolume 1: Advanced Computational Mechanics; Advanced Simulation-Based Engineering Sciences; Virtual and Augmented Reality; Applied Solid Mechanics and Material Processing; Dynamical Systems and Control · 2012
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsWeldingMagnetic fieldMATLABCoupling (piping)Finite element methodMechanicsSoftwareMechanical engineeringDeformation (meteorology)Tube (container)Computer scienceStructural engineeringMaterials scienceEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Magnetic pulse process is used in the forming and welding processes. In order to predict the welding conditions, it is necessary to have an accurate modeling, which involves a coupling between magnetic and mechanical phenomena. In a first step, a numerical modeling of the magnetic field has been developed in the finite elements software ANSYS™, and the forces exerted on a tube have been predicted. The model has been validated by comparison with similar models. The influences of the different parameters have been studied. Then, the deformation of this tube has been predicted by a dynamical model in the finite elements software ABAQUS/Explicit™. As the tube shrinks, the mechanical and magnetic computings must be sequentially coupled in order to predict the forces exerted during the motion. Software MATLAB™ is used to couple the two models in the two softwares.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.232
Teacher spread0.221 · 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.

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

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueVolume 1: Advanced Computational Mechanics; Advanced Simulation-Based Engineering Sciences; Virtual and Augmented Reality; Applied Solid Mechanics and Material Processing; Dynamical Systems and ControlSame topicMetal Forming Simulation TechniquesFrench-language works237,207