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The Dispersion Estimation Method for Assembly Stress of Silicone Rubber Foam Pad Based on Extreme Value Distribution

2013· article· en· W1986924311 on OpenAlexaff
Fei Wang, Fang Mei Wan

Bibliographic record

VenueApplied Mechanics and Materials · 2013
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsCanadian Association of Emergency Physicians
FundersScience and Technology Development FundChina Academy of Engineering Physics
KeywordsSilicone rubberMonte Carlo methodReliability (semiconductor)Dispersion (optics)Stress (linguistics)Extreme value theoryMaterials scienceNatural rubberStructural engineeringComposite materialStatisticsMathematicsEngineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

The silicone rubber foam is a suitable material used for heat insulation and vibration reduction. Because of dispersion of its mechanical character, it is difficult to quantify the assemble stress in reliability analysis. Based on reduced hyper-foam Ogden Model, the interval of model parameter is obtained through fitting the test data, and the predicted assemble stress distribution is also achieved by Monte-Carlo random simulation method. Then, the regression analysis of predicted data estimated is introduced by the extreme value distribution. The predicted assembly stress distribution described in this paper achieves high confidence by the extreme value distribution. Thus, combined with strain uncertain quantification, the reliability analysis of assemble stress can be performed.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.322

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 head0.216
Teacher spread0.207 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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