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Coupling of Mold Flow Simulations with Two-Scale Structural Mechanical Simulations for Long Fiber Reinforced Thermoplastics

2015· article· en· W2243575382 on OpenAlexaff
Fabian Buck, Barthel Brylka, Viktor Müller, Timo Müller, Andrew N. Hrymak, Frank Henning, Thomas Böhlke

Bibliographic record

VenueMaterials science forum · 2015
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsWestern University
FundersDeutsche Forschungsgemeinschaft
KeywordsMaterials scienceFiberComposite materialOrientation (vector space)BendingCoupling (piping)Flow (mathematics)MoldMechanicsGeometry

Abstract

fetched live from OpenAlex

The entire simulation process for long fiber reinforced thermoplastics is examined to determine the effective mechanical properties which are influenced by the microstructural fiber orientation state. Therefore, flow and fiber orientation simulations are conducted and the obtained fiber orientation tensors are used in two-scale structural simulations. The fiber orientation distributions as well as the mechanical properties are compared with micro-computed tomography data and results from threepoint bending tests performed by dynamical mechanical analysis (DMA), respectively. The validated results show that prediction of the essential mechanical properties is possible with the applied combinated methods and that the knowledge of the fiber orientation and its gradients is of crucial importance for the entire simulation process.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.617

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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 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

Citations5
Published2015
Admission routes1
Has abstractyes

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