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Bending Behavior to Fracture of an Aluminium Alloy Involving Pre-Strain

2016· article· en· W2563881214 on OpenAlexaff
A. Pradeau, Sandrine Thuillier, Jeong Whan Yoon

Bibliographic record

VenueKey engineering materials · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMaterials scienceAluminium alloyAlloyAluminiumBendingSheet metalAnisotropyStrain hardening exponentPlasticityStructural engineeringShear (geology)Composite materialHardening (computing)EngineeringPhysics

Abstract

fetched live from OpenAlex

The work associated to this abstract is focused on the modelling of an aluminium alloy under the shape of sheet. It characterizes the mechanical behaviour up to rupture of an AA6016 alloy, taking into account the anisotropy and the hardening of the metal. The mechanical tests on which the model is based on consist of uniaxial tension, simple shear and hydraulic bulging performed at room temperature up to rupture, except for the simple shear. The numerical model is constituted of three parts. The choice of the model is suited for ductile fracture and allows for high flexibility, thanks to a total of 21 material parameters. The material parameter identification is realised through an inverse methodology. The objective of such an approach is to minimize iteratively the gap between the experimental and numerical outputs. Validation of the results is then done with the help of bending tests. The bending tests are performed with and without pre-strain in tension prior to the air-bending. Different amplitudes of pre-strain allows to reach rupture or not in bending, thus giving the possibility to find the value of the parameter controlling the non-linear accumulation of the damage. The correlation between experiments and simulations is proved to be successful and gives a very good representation of the mechanical behaviour of the aluminium alloy studied.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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