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Record W2944271244 · doi:10.1080/21693277.2019.1608330

Parameters for the FEA simulations of single point incremental forming

2019· article· en· W2944271244 on OpenAlexafffund
Pranav Gupta, J. Jeswiet

Bibliographic record

VenueProduction & Manufacturing Research · 2019
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSheet metalForming processesIncremental sheet formingPoint (geometry)Finite element methodSingle pointProcess (computing)Deformation (meteorology)Multi pointShell (structure)Mechanical engineeringComputer scienceStructural engineeringGeometryMaterials scienceEngineeringComputer simulationSimulationMathematicsComposite material

Abstract

fetched live from OpenAlex

Single point incremental forming has shown immense potential in the manufacture of prototypes. The sheet metal deformation in this technique is carried out by a small hemispherical or flat tool that moves from the sheet periphery to the sheet center while also pushing the sheet down. Simulations of single point incremental forming can be a tedious task since the area required to be formed is large and the two sheet contact area is fairly small. The effect of tool however, affects the entire geometry by causing deflections and radial strains. Hence, it becomes essential to understand the effect of certain parameters that influence the simulation results for this forming process. The paper aims to study factors influencing the simulation of single point incremental forming process. It gives a description of techniques employed to simulate non-linear behavior of sheet metal forming along with the implicit and explicit methodology. Information on the selection of element type including Reduced Enhanced Solid-Shell element is presented. Techniques employed to avoid locking are also discussed.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.083
GPT teacher head0.338
Teacher spread0.255 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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