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Prediction of Sheet Metal Forming Limits in Multistage Forming Processes

2018· article· en· W2892975501 on OpenAlexaff
Morteza Nurcheshmeh, D Green, Chris Byrne, Anderson Závoli Habib

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFormabilityHydroformingSheet metalMaterials scienceForming processesStress (linguistics)Plane stressDeformation (meteorology)BendingComposite materialStructural engineeringTube (container)Finite element methodEngineering

Abstract

fetched live from OpenAlex

A three-dimensional stress state was implemented in a modified version of the Marciniak and Kuczynski model to predict Forming Limit Curves (FLC) with different through thickness stress values, and the sensitivity of the predicted FLC to the applied out-of-plane stress component was analyzed. Furthermore, the effect of normal stress on the formability of sheet metals under non-proportional loading was investigated. It was previously found that the influence of normal stress on the formability of sheet metals decreases with increased pre-strain, when the same normal stress is applied during both stages of deformation. For multistage metal forming processes, such as tube bending and hydroforming, the first forming stage can be performed without considering the normal stress, whereas the normal stress must be considered in the second forming stage. Alternately, different levels of normal stress may be applied in each forming stage. The present work aims to understand the influence of the normal stress applied in different stages of deformation on the sheet forming limits. Different normal stresses were applied during either the first or second forming stage, and the predicted final stress forming limits were compared in order to determine the influence of the normal stress for non-linear strain paths.

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 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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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