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Record W4312662634 · doi:10.31399/asm.cp.itsc2009p0456

Predict Flexural Rigidity of Sandwich Structures Using Mechanical Properties of the Constituents

2009· article· en· W4312662634 on OpenAlexaff
Fardad Azarmi, Thomas W. Coyle, J. Mostaghimi

Bibliographic record

VenueThermal spray · 2009
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlexural rigidityMaterials scienceAlloyComposite materialFlexural strengthRigidity (electromagnetism)Ultimate tensile strengthCoatingFlexural modulusElastic modulusCore (optical fiber)Compression (physics)ModulusSandwich-structured compositeStructural engineering

Abstract

fetched live from OpenAlex

Abstract Recently, an advanced technique was developed to fabricate sandwich structures for high temperature applications by depositing alloy 625 skins on Ni alloy foam core by thermal spraying. This study tries to utilize an analytical model to estimate the mechanical performance of these structures based on the mechanical properties of the constituents. The mechanical behavior of the Ni alloy foam is assessed via compression testing, while tensile tests are used in the case of the alloy coating. The flexural rigidity of the sandwich structure is calculated using analytical models and experimentally obtained elastic moduli of the alloy 625 coating and Ni alloy foam. The model is also used to calculate the flexural rigidity of sandwich samples with different skin thicknesses to check the accuracy of the model and to understand the effect of skin thickness on the predicted mechanical performance of sandwich structures. The effect of heat treatment on the mechanical behavior of sandwich structures is investigated as well.

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 score0.015
Threshold uncertainty score0.266

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 head0.015
GPT teacher head0.204
Teacher spread0.189 · 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
Published2009
Admission routes1
Has abstractyes

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