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FE Study for Reducing Forming Forces and Flat End Areas of Cylindrical Shapes Obtained by the Roll-Bending Process

2014· article· en· W2244643682 on OpenAlexafffund
Henri Champliaud Quan Hoang Tran, Zhengkun Feng, Thiên My Dao

Bibliographic record

VenueJournal of Mechanics Engineering and Automation · 2014
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBendingProcess (computing)Structural engineeringMechanical engineeringMaterials scienceRoll formingEngineeringMechanicsPhysicsComputer science

Abstract

fetched live from OpenAlex

A roll-bending process that minimizes the flat areas on the leading and trailing ends of formed plates will produce more accurate and easier assemble final shapes. There are several methods of minimizing flat areas, but they are costly or difficult to apply for thick plates. This study proposes a new, simple approach that reduces these flat areas. This approach includes moving the bottom roll slightly along the feeding direction and adjusting the bottom roll location. Sensitivity analyses were performed using a developed 3D dynamic FE (finite element) model of an asymmetrical roll-bending process in the Ansys/LS-Dyna software package. Simulations were validated by experiments run on an instrumented roll-bending machine. The FE results indicate that this new approach not only minimizes the flat areas but also reduces the forming forces.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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